Adaptive beamforming systems and methods for communication systems

[0001] This application claims the benefit of U.S. Provisional Application No. 60/772,735, entitled “Quantized Feedback Optimal Adaptive Beamforming for Frequency Division Duplex Systems,” filed on Feb. 13, 2006, which is incorporated herein by reference.

[0002] The present invention is directed, in general, to communication systems and, in an exemplary embodiment, to a system and method for adaptively selecting antenna weighting parameters for a multiple-antenna wireless communication system.

[0003] The communication of information is a necessity of modern society, which is enabled through the operation of a communication system. Information is communicated between a sending station and a receiving station by way of communication channels. The sending station, if necessary, converts the information into a form for communication over the communication channels. The receiving station detects and recovers the information for the benefit of a user. A wide variety of different types of communication systems have been developed and are regularly employed to effectuate communication between the sending and receiving stations. New types of communication systems have been and continue to be developed and constructed as a result of advancements in communication technologies.

[0004] An exemplary communication system is a radio communication system in which a communication channel is defined upon a radio link extending between the sending and receiving stations. The radio communication systems are amenable to implementation as mobile communication systems wherein radio links, rather than fixed, wireline connections, are employed to define communication channels. A cellular communication system is an example of a radio communication system that has achieved significant levels of usage. Cellular communication systems have been installed throughout significant parts of the populated world. Various cellular communication standards have been promulgated, setting forth the operational parameters of different types of cellular communication systems.

[0005] Generally, a cellular communication system includes a network infrastructure that includes a plurality of base stations that are positioned at spaced-apart locations throughout a geographic area. Each of the base stations defines an area, referred to as a cell, from which the cellular communication system derives its name. The network infrastructure of which the base stations form portions is coupled to a core network such as a packet data backbone or a public-switched telephone network. Communication devices such as computer servers, telephone stations, etc., are, in turn, coupled to the core network and are capable of communication by way of the network infrastructure and the core network. Portable transceivers, referred to as mobile stations, communicate with the base stations by way of radio links forming portions of an electromagnetic spectrum. The use of the cellular communication system is permitted, typically, pursuant to a service subscription and users (referred to as subscribers) that communicate by way of the cellular communication system through utilization of the mobile stations.

[0006] Information communicated over a radio link is susceptible to distortion such as dispersion as a result of non-ideal communication conditions. The distortion causes the information delivered to a receiving station to differ from the corresponding information transmitted by the sending station. If the distortion is significant, the informational content will not be accurately recovered at the receiving station. For instance, fading caused by multi-path transmission distorts information communicated over a communication channel. If the communication channel exhibits significant levels of fading, the informational content may not be recoverable.

[0007] Various techniques such as spatial diversity are employed to compensate for, or otherwise overcome, the distortion introduced upon the information transmitted over a communication channel to the receiving station. Spatial diversity is created through the use, at a sending station, of more than one transmit antenna from which information is transmitted, thereby creating spatial redundancy therefrom. The antennas are typically separated by distances sufficient to ensure that the information communicated by respective antennas fades in an uncorrelated manner. Additionally, the receiving stations sometimes use more than one receive antenna, preferably separated by appropriate distances.

[0008] Communication systems that utilize both multiple transmitting antennas and multiple receiving antennas are often referred to as being multiple-input, multiple-output (“MIMO”) systems. Communications in a MIMO system provide the possibility that higher overall capacity of the system, relative to conventional systems, can be achieved. As a result, an increased number of users may be serviced or more data throughput may be provided with improved reliability for each user. The advantages provided through the use of spatial diversity are further enhanced if the sending station is provided with information about the state or performance of the communication channel between the sending and receiving stations.

[0009] A sending station generally cannot measure channel characteristics of the communication channel directly, such as a channel correlation matrix representing a product of channel impulse response components for the multiple transmitting antennas. Thus, the receiving station typically measures the channel characteristics of the communication channel. In two-way communication systems, measurements made at the receiving station can be returned to the sending station to provide the channel characteristics to the sending station. Communication systems that provide this type of information to multiple-antenna sending stations are referred to as closed-loop transmit diversity systems. Communication channels extending from the network infrastructure of a cellular communication system to a mobile station are sometimes referred to as being downlink, or forward-link, channels. Conversely, the channels extending from the mobile station back to the network infrastructure are sometimes referred to as being uplink, or reverse-link, channels.

[0010] The feedback information returned to the sending station (e.g., the network infrastructure such as a base station) from the receiving station (e.g., a mobile station) is used to select values of antenna weightings. The weightings are values including phase delays by which information signals provided to individual antennas are weighted prior to their communication over a communication channel to the mobile station. A goal is to weight the information signals in amplitude and phase applied to the antennas in a manner that best facilitates communication of the information to the receiving station. The weighting values of the antenna approach a conjugate of the subspace spanned by a downlink channel covariance matrix. Estimation of the antenna weightings can be formulated as a transmission subspace tracking procedure. Several closed-loop transmit diversity procedures may be utilized.

[0011] As an example, transmit adaptive array (“TxAA”), eigenbeam former, and other techniques may be employed to advantage. Existing techniques, however, suffer from various deficiencies. For instance, the TxAA procedure fails to take into account a long-term covariance matrix of the communication channel in the selection of the antenna weightings. Additionally, the use of an eigenbeam former technique is dependent upon the number of antennas of the sending station. When the number of antennas increases, the complexity of such a technique increases rapidly.

[0012] Considering the limitations as described above, a system and method to control antenna weighting parameters for multiple antennas employed in a wireless communication system is not presently available for the more severe applications that lie ahead. Accordingly, what is needed in the art is a system that adaptively selects antenna weighting parameters and sends a quantized increment vector back to the transmitter, provides fast and global convergence to the ideal antenna weights, and requires minimal data rate to communicate the results from the receiver to the transmitter, overcoming many of the aforementioned limitations. In accordance therewith, a wireless communication system employing multiple antennas would benefit from such an adaptive arrangement without incurring unnecessary uplink bandwidth or the need to compromise signal strength at the receiving antenna.

[0013] These and other problems are generally solved or circumvented, and technical advantages are generally achieved, by advantageous embodiments of the present invention which includes a wireless communication system including a receiving station and a base station including multiple antennas weighted by corresponding weighting elements. In one embodiment, the receiving station includes a detector that measures a downlink channel correlation matrix for multiple antennas of the base station, computes an antenna weight increment vector normal to a previous antenna weight vector rendering positive a directional derivative of total received power from the multiple antennas of the base station, and quantizes the antenna weight increment vector to produce a quantized antenna weight increment vector. The receiving station also includes a transmitter that sends the quantized antenna weight increment vector to the base station. In one embodiment, the base station includes a beamformer selector that re-orthogonalizes the quantized antenna weight increment vector. The base station also includes a weight vector modifier that calculates an antenna weight vector by adding an increment to the previous antenna weight vector proportional to the re-orthogonalized quantized antenna weight increment vector and renormalizes the antenna weight vector to unit length.

[0014] The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures or processes for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims.

[0026] The making and using of the presently preferred embodiments are discussed in detail below. It should be appreciated, however, that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed are merely illustrative of specific ways to make and use the invention, and do not limit the scope of the invention.

[0027] The present invention will be described with respect to exemplary embodiments in a specific context of beamforming systems such as a quantized-feedback optimal adaptive (“QFOA”) beamforming system that adaptively transmits a beamforming vector in a multiple-antenna frequency division duplex (“FDD”) system. The system is referred to as a quantized-feedback optimal adaptive beamforming solution because the system and methodology adapt a transmit beamforming vector by computing a direction of preferable adaptation and feeding a quantized increment vector back to a transmitter. The system offers an adaptive diversity technique with global convergence. At each step of the adaptation, the quantized increment vector is projected onto a hyperplane tangent to a constraint hypersurface. The tangential component is then used to adapt the antenna weight. Since the increment vector points in a direction that yields a sufficient gradient of the objective function subject to the constraint, the convergence speed is improved. Fast convergence improves the system performance during, for instance, a start-up period and helps remove the need to continuously track the antenna weights during intermittent periods wherein the transmitter does not transmit to a mobile station, or while the connection is temporarily suspended or idle. High convergence speed also provides a fast tracking capability demanded by high mobility applications. Additionally, several quantization methods are described herein that provide global convergence irrespective of a quantization error, since the gradient is preferably positive at any nonstationary point in a solution space.

[0028] For clarity, the description that follows will concentrate on the downlink case so that the transmitter weight adaptation is performed at a base station (“BS”). The receiving station, in this case, is located at a mobile station (“MS”). In addition, it is advantageous to perform the adaptation at the base stations as provided in “Transmit Diversity in 3G CDMA Systems,” by R. T. Derryberry, S. D. Gray, D. M. Ionescu, G. Mandyam, and B. Raghothaman, IEEE Communications Magazine, pp. 68-75 (April 2002), which is incorporated herein by reference.

[0029] The capacity of the future wireless Internet depends strongly on a channel capacity of a wireless interface between access terminals (such as a mobile station) and access networks (such as a base station). As mentioned above, one of the methodologies to achieve an increase in capacity of a fading channel is to employ diversity techniques involving multi-antenna arrays at the transmitter or the receiver. When an antenna array is used at the receiver, multi-dimensional signal processing is employed to improve the detection quality at the cost of an increased receiver complexity. When the transmitter broadcasts from a multi-antenna array, transmit diversity techniques can be employed to increase the signal quality or the information rate. A methodology for transmit diversity is space-time coding including the pioneering work of S. M. Alamouti, “A Simple Transmit Diversity Technique for Wireless Communications,” IEEE J. Select. Areas in Comm., vol. 16, pp. 1451-1458, October 1998, followed by contributions such as V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-Time Block Codes from Orthogonal Designs,” IEEE Trans. Info. Theory, vol. 45, pp. 1456-1467, July 1999, C. B. Papadias and G. J. Foschini, “A Space-Time Coding Approach for Systems Employing Four Transmit Antennas,” in Proc. IEEE Int. Conf. Acoust., Speech, Sig. Processing, vol. 4, pp. 2481-2484, May 7-11, 2001, and C. B. Papadias and G. J. Foschini, “Capacity-Approaching Space-Time Codes for Systems Employing Four Transmitter Antennas,” IEEE Trans. Info. Theory, vol. 49, pp. 726-732, March 2003, all of which are incorporated herein by reference.

[0030] Space-time coding employs multiple transmit antennas. When a receiver is equipped with multiple antennas, a coding gain is achieved by employing a signal allocating scheme across multiple antennas and multiple symbol periods. (See, also, G. J. Foschini, “Layered Space Time Architecture for Wireless Communication in a Fading Environment When Using Multi-Element Antennas,” IEEE J. Select. Areas in Comm., pp. 1437-1450, 1996, which is incorporated herein by reference). A diversity gain is also achieved by virtue of independent or weakly correlated fading among the antennas. The coding gain, however, is absent when only one receive antenna is used, rendering any gain possible only via transmit diversity.

[0031] Parallel to space-time coding is a beamforming methodology, which employs multiple, transmit antennas. A crucial difference between space-time coding and beamforming is that in the latter, the antennas transmit the same signal weighted by a different scale factor wherein the scale factor is a complex weighting parameter. The weights are constrained due to a limit on the total transmit power, but the distribution of the weights can be “steered” in such a way that the received signal is improved according to a certain criterion such as the total received power which, under certain conditions, is equivalent to improving the effective signal-to-noise ratio (“SNR”) and the channel capacity. When the set of weights are appropriately selected and tracked in the case of fading, both fading diversity and steering gains can be achieved.

[0032] To perform the aforementioned beamforming methodology, direct or indirect knowledge about the downlink channel is necessary. Some of this knowledge can be extracted from the uplink channel information, provided that the uplink and downlink channels are correlated. (See, e.g., G. G. Raleigh, S. N. Diggavi, V. K. Jones, and A. Paulraj (“G. G. Raleigh, et al.”), “A Blind Adaptive Transmit Antenna Algorithm for Wireless Communication,” in Proc. IEEE Int. Comm. Conf., vol. 3, pp. 1494-1499, Jun. 18-22, 1995, which is incorporated herein by reference). In frequency division duplex (“FDD”) systems wherein the uplink-downlink frequency separation is normally larger than the signal bandwidth, the correlation between the uplink and downlink channels is weak or non-existent, although the long term correlation exists as supported by the works of G. G. Raleigh, et al. Unfortunately, long term correlation diminishes the fading diversity gain.

[0033] Since beamforming employs some detailed knowledge about the downlink channel, it has been proposed in the literature to employ a feedback path from the receiver to the transmitter via which downlink channel information estimated based on the downlink pilots is sent. (See, e.g., D. Gerlach and A. Paulraj, “Adaptive Transmitting Antenna Arrays with Feedback,” IEEE Sig. Proc. Letters, vol. 1, pp. 1437-1450, October 1994, R. W. Heath Jr. and A. Paulraj, “A Simple Scheme for Transmit Diversity Using Partial Channel Feedback,” in Proc. Asilomar Conf. Signals, Systems & Computers, vol. 2, pp. 1073-1078, Nov. 1-4, 1998, D. Gerlach and A. Paulraj, “Spectrum Reuse Using Transmitting Antenna Arrays with Feedback,” in Proc. IEEE Int. Conf. Acoust., Speech, Sig. Processing, vol. 4, pp. IV97-IV100, Apr. 19-22, 1994, and J. W. Liang and A. J. Paulraj, “Forward Link Antenna Diversity Using Feedback for Indoor Communication Systems,” Proc. IEEE Int. Conf. Acoust., Speech, Sig. Processing, vol. 3, pp. 1753-1755, May 1995, all of which are incorporated herein by reference).

[0034] To be practical, due to bandwidth constraints, the feedback rate should be reasonably low. The beamforming operation at a base station should therefore be designed in such a way that the base station relies on limited feedback information from the mobile station. In this respect, B. C. Banister, et al. proposed to adaptively select the total delivered power by employing the random perturbation gradient approximation principle. (See, e.g., B. C. Banister and J. R. Zeidler (“B. C. Banister, et al.”), “A Simple Gradient Sign Algorithm for Transmit Antenna Weight Adaptation with Feedback,” IEEE Trans. Sig. Processing., vol. 51, pp. 1156-1171, May 2003, which is incorporated herein by reference). At each adaptation step, a randomly generated perturbation vector is added and then subtracted from the previous antenna weight vector. The addition and subtraction results are used as the antenna weight vector in two consecutive transmission half-slots. The term half-slot generally refers to or signifies a time duration sufficiently long for the purpose of channel and power estimation. The information fed back from the mobile station is a one-bit flag indicating which half-slot has the higher delivered power. The next weight vector is updated to the one used in the half-slot with higher delivered power.

[0035] A similar technique has been proposed by B. Raghothaman (see, e.g., B. Raghothaman, “Deterministic Perturbation Gradient Approximation for Transmission Subspace Tracking in FDD-CDMA,” in Proc. IEEE Int. Comm. Conf., vol. 4, pp. 2450-2454, May 11-15, 2003, and as further described in U.S. Pat. No. 6,842,632 entitled “Apparatus, and Associated Method, for Facilitating Antenna Weight Selection Utilizing Deterministic Perturbation Gradient Approximation,” to Raghothaman, et al., issued Jan. 11, 2005, which are incorporated herein by reference), which employs the fact that the mean perturbation gradient aligns with the true gradient of the objective function using an orthogonal perturbation set. (See, e.g., A. Cantoni, “Application of Orthogonal Perturbation Sequences to Adaptive Beamforming,” IEEE Trans. on Antennas and Propagation, vol. 28, pp. 191-202, March 1980, which is incorporated herein by reference).

[0036] A related application to B. Raghothaman is provided in U.S. Patent Application Publication No. 2005/0064908 entitled “Apparatus, and Associated Method, for Assigning Data to Transmit Antennas of a Multiple Transmit Antenna Transmitter,” to Boariu, et al., published Mar. 24, 2005, which is incorporated herein by reference. As described above, a perturbation gradient approximation technique is used for the selection of antenna weightings at a sending station employing transmit antenna diversity. In accordance therewith, B. Raghothaman uses an approximation technique that facilitates the selection of the antenna weighting values to enhance communications between a sending station and a receiving station. A perturbation vector is selected at the sending station for communication over the communication channel to the receiving station. The perturbation vector is selected in an order from a selected set of vectors formed from vector values. The antenna weightings for the antennas of the sending station are perturbed in a first manner during a first portion of a time period and in a second manner during a second portion of a time period. During the first portion of the time slot, the antenna weightings are perturbed by the perturbation vector in a positive direction. During the second portion of the time slot, the perturbation vector is applied to the antenna weightings to perturb the weightings in a negative direction.

[0037] The receiving station measures power levels associated with the information communicated by the sending station. Separate power level measurements are made during the first and second portions of the time slot. Differences between the power levels measured during the separate portions of the time slot are determined and transmitted back to the sending station. The sending station detects values of the calculated differences made at the receiving station and employs the values to adjust the antenna weightings.

[0038] Thus, the deterministic perturbation gradient approximation technique is typically used in cellular communication systems having a base station that utilizes spatial diversity. Closed-loop transmit diversity is provided to select antenna weightings by which to weight downlink signals. The perturbation vectors are applied to the antenna weightings in positive and negative directions during separate portions of a time period. The weighted signals are sent by the base station to the mobile station for detection thereof. The mobile station measures power levels of the signals detected during the first and second portions of the time slot and returns values of differences in the power levels to the base station. The values returned to the base station are used to adjust the antenna weightings at the base station. Inasmuch as deterministic perturbation gradient approximation techniques are used, a long-term covariance matrix is tracked and utilized to select the antenna weightings.

[0039] Referring now to FIG. 1, illustrated is a block diagram of an embodiment of a communication system that provides radio communication between communication stations via communication channels employing a deterministic perturbation gradient approximation. The communication system includes a base station 100 and a mobile station 170. The communication channels are defined by radio links such as forward-link channels 105 and reverse-link channels 110. Information sent to the mobile station 170 is communicated by the base station 100 over the forward-link channels 105 and information originated at the mobile station 170 for communication to the base station 100 is communicated over reverse-link channels 110. The communication system may be a cellular communication system constructed pursuant to any of a number of different cellular communication standards. For instance, the base station and mobile station may be operable in a code division multiple access (“CDMA”) communication system such as a third generation (“3G”) CDMA communication.

[0040] The base station 100 forms part of a radio access network that also includes a radio network controller (“RNC”) 115 coupled to a gateway 120 and a mobile switching center (“MSC”) 125. The gateway 120 is coupled to a packet data network (“PDN”) 130 such as the Internet and the mobile switching center 125 is coupled to a public switched telephone network (“PSTN”) 135. A correspondent node (“CN”) 137 is coupled to the packet data network 130 and to the PSTN 135. The correspondent node 137 represents a data source or a data destination from which, or to which, information is routed during operation of the communication system.

[0041] The base station 100 includes a receiver 140 and a transmitter 145. A forward-link signal to be communicated by the base station 100 to the mobile station 170 is converted into a format for communication over the forward-link channels 105 by the transmitter 145. Closed-loop feedback information is returned by the mobile station 170 to the base station 100 by way of the reverse-link channels 110. The mobile station 170 also includes a receiver 175 and a transmitter 180. The receiver 175 operates to receive, and operate upon, the forward-link signals transmitted by the base station 100 over the forward-link channels 105, and the transmitter 180 operates to transmit reverse-link signals over the reverse-link channels 110 to the base station 100.

[0042] The base station 100 and the mobile station 170 include multiple antennas, and the base station 100 and the mobile station 170 combination forms a multiple-input, multiple-output (“MIMO”) system. For purposes of clarity, the base station 100 includes M base station antennas designated 147-1 to 147-M (hereinafter referenced as base station antennas 147). Also for purposes of clarity, the mobile station 170 includes N mobile station antennas designated 185-1 to 185-N (hereinafter referenced as mobile station antennas 185).

[0043] The base station transmitter 145 includes an encoder 150 that encodes data to form encoded data. The encoded data is provided to an up-mixer 155 with an up-mixing carrier v(t) to generate an up-mixed signal. The up-mixed signal is provided via weighting elements (two of which are referenced and designated as first and second weighting elements 160, 162, respectively) on separate branches to ones of the base station antennas 147. Once the up-mixed signals are weighted, the weighted signals are applied to the base station antennas 147 for transmission to the mobile station 170. Of course, other operations may be performed on the weighted signals prior to transmission to the mobile station 170.

[0044] The base station 100 also includes a deterministic perturbation gradient approximation system that adjusts the values of the weightings applied to the first and second weighting elements 160, 162 in a manner that enhances antenna weighting selection pursuant to a closed-loop transmit diversity system. The deterministic perturbation gradient approximation system (which may be embodied in hardware, software, or combinations thereof) includes a perturbation vector selector (“PVS”) 165 that operates to select perturbation vectors formed of vector values (indicating a perturbed amplitude and phase) retrieved from a perturbation vector buffer (“PVB”) 167. The perturbation vectors selected by the perturbation vector selector 165 are provided to a perturbation vector applicator (“PVA”) 169 for application to the first and second weighting elements 160, 162. The perturbation vectors perturb the weightings of the first and second weighting elements 160, 162 and, in turn, the values of the signals transmitted by the base station's antennas 147. The forward-link signals generated on the forward-link channels 105, weighted with the perturbation vectors, are delivered to the mobile station 170.

[0045] The mobile station 170 includes a detector 190 (which may be a subsystem of the receiver 175) that detects and measures characteristics representing power levels of the perturbations of the forward-link signals (again, weighted forward-link signals) transmitted by the base station 100. The characteristics associated with the forward-link signals are thereafter transmitted by the mobile station transmitter 180 to the base station 100. The characteristics are thereafter employed by the base station 100 to adjust the weightings of the first and second weighting elements 160, 162 for the base station antennas 147 to refine the forward-link signals.

[0046] The deterministic perturbation gradient approximation system operates to provide a deterministic perturbation gradient approximation that provides tracking of long-term feedback. The deterministic perturbation gradient approximation technique may build upon stochastic perturbation gradient approximations (“SPGAs”) and may use a procedure referred to as gradient descent. Gradient descent involves adaptively converging to a point in a vector space corresponding to the global minimum (or maximum) of a cost function such as total received power defined on the space. At each iteration of the adaptation, an estimate of the gradient of the cost function is formed, and the estimate of the optimal vector is revised such that it moves in the direction of the gradient vector. The process can be visualized in three dimensions involving a 2-D vector space, as moving closer to the bottom of a cost function bowl at each iteration.

[0047] The most widely used gradient search technique is the stochastic gradient search, which is applied in the least mean squares algorithm for adaptive finite impulse response (“FIR”) filtering. One of the features of adaptive time-domain FIR filtering is that an input stochastic vector process is acting upon the filter, which is dimensionality analogous to the filter, and can be used to estimate the cost function gradient vector. In certain other situations, like neural network learning, it is necessary to use other methods for arriving at the gradient vector. One such method is called the simultaneous stochastic perturbation gradient approximation technique, in which, at each iteration, the effect of a stochastic perturbation on the cost function is studied. Based thereon, an estimate of an optimal vector is moved toward or away from the direction of the “random” perturbation vector.

[0048] The stochastic perturbation technique mentioned above has been applied to the problem of transmission subspace tracking for closed-loop transmit diversity and MIMO systems. In order for this SPGA technique to be possible, user-specific pilot signals are employed, which carry the perturbed weights based on which the cost function is estimated. The user-specific pilot signals are also used to coherently demodulate the received signal, since the average of the user-specific pilot signal over two slots provides the weighted composite channel estimate necessary for coherent demodulation. In many proposals for high-speed packet access wireless communication systems, however, there is no provision for user-specific pilot signals. Hence, the existing SPGA techniques cannot be implemented in such situations where there is no user-specific pilot signal. Another disadvantage of the existing SPGA techniques is that the user-specific pilot signals are usually transmitted with the same power as the traffic signal, which is quite low when compared to the non-user-specific pilot signals, henceforth referred to as common pilot signals. This arrangement leads to a degradation in the channel estimate and hence the performance of the communication link as such.

[0049] A possible method for increasing the reliability of the channel estimate is to use a combination of the channel estimates from the common pilot signals along with an estimate of the weights applied at the transmitter 145 of the base station 100. These weights can be known to the mobile station 170 if the same set of random perturbation vectors applied at the transmitter 145 of the base station 100 can be replicated at the receiver 175 of the mobile station 170. The aforementioned technique involves operating a complex random number generator at both the base station 100 and the mobile station 170 in a synchronized manner. The complexity and synchronization issues make this an undesirable alternative.

[0050] The use of the deterministic perturbation gradient approximation technique overcomes the limitations as set forth above. The deterministic perturbation makes it unnecessary to operate two synchronized random vector generators. Also, a method of extracting the cost function from the traffic signal itself may be employed, thereby rendering a user-specific pilot signal unnecessary. B. Raghothaman provides support to estimate the optimal antenna weights (designated “w”) for transmission between the base station 100 and the mobile station 170 including quantitative support via equations. In accordance therewith, a perturbation method is employed that provides an approximation to the gradient of the cost function to the transmitter with a feedback signal.

[0051] The convergence of the deterministic perturbation gradient approximation method can be shown to be similar to that of the stochastic perturbation approach. The randomness of the perturbation has been stated as a condition for convergence. One of the considerations seems to be that a deterministic perturbation will lead to a biased estimate. Another consideration is that only a random perturbation may lead to convergence to a global minimum. From this point of view, the iterative procedure may be trapped in a local minimum on the condition that the cost function surface is flat in the directions pertaining to each of the perturbation vectors, at that local minimum. The probability of such an occurrence is relatively small, especially in a time varying fading channel environment. Additionally as described in B. Raghothaman, an approach to obtaining the cost function is to use the traffic channel itself. The only disadvantage of the usage of the traffic channel itself is that the actual weights applied are perturbed from the values dictated by the adaptation.

[0052] While the techniques of B. C. Banister, et al and B. Raghothaman employ a similar feedback, the deterministic perturbation method of B. Raghothaman turns out to have a faster average convergence speed. In addition, deterministic perturbation allows both the mobile station and the base station to synchronize the adaptation, a feature that is difficult to implement with the random perturbation method of B. C. Banister, et al.

[0053] Referring now to FIG. 2, illustrated is a block diagram of a communication system including an embodiment of a tangential perturbation gradient system. It should be understood that the tangential perturbation gradient system may be a deterministic or random perturbation gradient system. The communication system includes a base station 200 and a receiving station (e.g., mobile station 270). The communication channels are defined by radio links such as forward-link channels 205 and reverse-link channels 210. Information sent to the mobile station 270 is communicated by the base station 200 over the forward-link channels 205 and information originated at the mobile station 270 for communication to the base station 200 is communicated over reverse-link channels 210.

[0054] The communication system may be a cellular communication system constructed pursuant to any of a number of different cellular communication standards. For instance, the base station and mobile station may be operable in a code division multiple access (“CDMA”) communication system such as a third generation (“3G”) CDMA communication that provides for 1xEV-DV (“EVolution, Data and Voice”) data and voice communications. Those skilled in the art, however, realize that the tangential perturbation gradient system and other perturbation gradient systems described herein are employable in any number of communication systems including other types of cellular communication systems such as a global system for mobile (“GSM”) communications systems that provides for general packet radio service or enhanced data for GSM evolution data services, each of which also provides for data communications. The communication system is also representative of other types of radio and other communication systems in which data is communicated over channels that are susceptible to distortion caused by fading or other conditions. Those skilled in the art should understand that the principles described herein are operable in any communication system employing closed-loop transmit diversity.

[0055] The base station 200 forms part of a radio access network that also includes a radio network controller (“RNC”) 215 coupled to a gateway 220 and a mobile switching center (“MSC”) 225. The gateway 220 is coupled to a packet data network (“PDN”) 230 such as the Internet backbone and the mobile switching center 225 is coupled to a public switched telephone network (“PSTN”) 235. A correspondent node (“CN”) 237 is coupled to the packet data network 230 and to the PSTN 235. The correspondent node 237 represents a data source or a data destination from which, or to which, information is routed during operation of the communication system.

[0056] The base station 200 includes a receiver 240 and a transmitter 245. A forward-link signal to be communicated by the base station 200 to the mobile station 270 is converted into a format for communication over the forward-link channels 205 by the transmitter 245. Closed-loop feedback information is returned by the mobile station 270 to the base station 200 by way of the reverse-link channels 210. The mobile station 270 also includes a receiver 275 and a transmitter 280. The receiver 275 operates to receive, and operate upon, the forward-link signals transmitted by the base station 200 over the forward-link channels 205, and the transmitter 280 operates to transmit reverse-link signals over the reverse-link channels 210 to the base station 200.

[0057] The base station 200 and the mobile station 270 include multiple antennas, and the base station 200 and mobile station 270 combination forms a multiple-input, multiple-output (“MIMO”) system. For purposes of clarity, the base station 200 includes M base station antennas designated 247-1 to 247-M (hereinafter referenced as base station antennas 247). Also for purposes of clarity, the mobile station 270 includes N mobile station antennas designated 285-1 to 285-N (hereinafter referenced as mobile station antennas 285).

[0058] The base station transmitter 245 includes an encoder 250 that encodes data to form encoded data. The encoded data is provided to an up-mixer 255 with an up-mixing constant v(t) to generate an up-mixed signal. The up-mixed signal is provided via weighting elements (two of which are referenced and designated as first and second weighting elements 260, 262, respectively) on separate branches to ones of the base station antennas 247. Once the up-mixed signals are weighted, the weighted signals are applied to the base station antennas 247 for transmission to the mobile station 270. Of course, other operations may be performed on the weighted signals prior to transmission to the mobile station 270.

[0059] The base station 200 also includes a tangential perturbation gradient system that adjusts the values of the weightings applied to the first and second weighting elements 260, 262 in a manner that enhances antenna weighting selection pursuant to a closed-loop transmit diversity system. The tangential perturbation gradient system (which may be embodied in hardware, software, or combinations thereof) includes a tangential perturbation vector selector (“TPVS”) 265 that operates to select a tangential component of perturbation vectors formed of vector values (indicating perturbed amplitude and phase) retrieved from a perturbation vector buffer (“PVB”) 267. The tangential component of the perturbation vectors selected by the perturbation vector selector 265 are provided to a tangential perturbation vector applicator (“TPVA”) 269 for application to the first and second weighting elements 260, 262. The tangential components of the perturbation vectors perturb the weightings of the first and second weighting elements 260, 262 and, in turn, the values of the signals transmitted by the base stations antennas 247. The forward-link signals generated on the forward-link channels 205, weighted with the tangential components of the perturbation vectors, are delivered to the mobile station 270.

[0060] The mobile station 270 includes a detector 290 (which may be a subsystem of the receiver 275) that detects and measures characteristics representing power levels of the perturbed forward-link signals (again, weighted forward-link signals) transmitted by the base station 200. The characteristics associated with the forward-link signals are thereafter transmitted by the mobile station transmitter 280 to the base station 200. The characteristics are thereafter employed by the base station 200 to adjust the weightings of the first and second weighting elements 260, 262 for the base station antennas 247 to refine the forward-link signals.

[0061] The tangential perturbation gradient system operates to provide a tangential perturbation gradient approximation that provides tracking of long-term feedback. The tangential perturbation gradient approximation technique may build upon stochastic perturbation gradient approximations (“SPGAs”) or deterministic perturbation gradient approximations selected from a deterministic set of perturbations and may use a procedure referred to as gradient descent.

[0062] Referring initially to the signaling model, consider a base station with M transmit antennas and a mobile station (“a receiving station”) with N receive antennas. Let x(n) denote the unit-power information sequence:

$\left(\mathrm{with}{\sigma }_x^2\stackrel{\Delta }{=}E\left[{x\left(n\right)}^2\right]=1\right)$

to be transmitted over the M antennas. Assuming transmit and receiver filters constitute a Nyquist pulse, a noiseless Nyquist-rate sampled sequence received at the r-th receive antenna can be modeled by:

$y_r\left(n\right)=\sum _{t=1}^Mw_t\sum _{l=0}^Dh_{r,t}\left(l\right)x\left(n-l\right)$

where w_t denotes the t-th transmit antenna weight, h_r,t(l) represents the discrete-time channel impulse response between the t-th transmit antenna and the r-th receive antenna. The delay spread of the channel impulse response is denoted by D. In order to constrain the total transmit power, the antenna weight vector, defined as:

$w\stackrel{\Delta }{=}{\left[w_1,w_2,\dots ,w_M\right]}^T,$

satisfies:

w^Hw=1.

At time n, the vector of observations from all the receive antennas is:

$y\left(n\right)\stackrel{\Delta }{=}{\left[y_1\left(n\right),y_2\left(n\right),\dots ,y_N\right]}^T.$

[0063] Assuming that the transmitted information sequence x(n) is white, the received signal power is:

$J\left(w\right)\stackrel{\Delta }{=}E\left[y^T\left(n\right)y^{*}\left(n\right)\right]=w^H\mathrm{Rw}$

where the channel correlation matrix R is given by:

$R=\sum _{r=1}^NH_r^{*}H_r^T,H_r=\left[\begin{array}{cccc}{h}_{r,1}\left(0\right)& h_{r,1}\left(1\right)& \cdots & h_{r,1}\left(D\right)\\ h_{r,2}\left(0\right)& h_{r,2}\left(1\right)& \cdots & h_{r,2}\left(D\right)\\ ⋮& ⋮& \cdots & ⋮\\ h_{r,M}\left(0\right)& h_{r,M}\left(1\right)& \cdots & h_{r,M}\left(D\right)\end{array}\right].$

It should be understood that the operators (.)*, (.)^T and (.)^H=((.)*)^T denote complex conjugation, transposition and hermitian, respectively. Note that in addition to the information sequence x(n), each transmit antenna also carries a pilot sequence covered by a predetermined spreading sequence. The channel impulse response for each transmit-receive antenna pair can be estimated from the associated pilot. It is important to point out that in multiple access systems, each mobile station associated with a different antenna weight vector w and the signal model above is applicable to any one mobile station. For notational simplicity, the time dependency of the channel matrix H_r has been suppressed. Nonetheless, in most cases of practical interest, the channel can be considered block-wise constant where the block duration is on the order of the channel coherence time, typically long enough for a channel estimation purpose.

[0064] An objective of the tangential perturbation gradient system and method is to increase the received power function J(w) subject to the constraint provided above. Even though the channel correlation matrix R can be estimated from the pilot sequences, it is often not practical due to bandwidth constraints to send the entire matrix to the base station. In order to adapt the antenna weight vector w at the base station, the mobile station can compute the values of the received power function J(w₊), J(w₋) and feed back the sign of J(w₊)−J(w₋) for any antenna weight vectors w₊, w₋ employed in place of a single weight vector w in two consecutive half-slots.

[0065] Turning now to FIG. 3, illustrated is a flow diagram of an embodiment of a method of operating a tangential perturbation gradient system. For purposes of simplicity, the circumstances wherein the perturbation vector is parallel to the antenna weight vector is not illustrated with respect to FIG. 3.

[0066] If k denotes a slot index, the tangential perturbation gradient system and method is provided as set forth below.

[0067] 1. Generate initial antenna weights w(1) with ∥w(1)∥=1.

[0068] 2. Set a slot index counter k=1.

[0069] 3. Generate a unit 2-norm (i.e., a Euclidean norm) perturbation vector d(k). This vector can be selected cyclically from a deterministic set that spans the entire complex vector space C^M, or can be generated randomly (see, for instance, the separate publications by B. C. Banister, et al. and B. Raghothaman referenced above).

[0070] 4. Compute a tangential perturbation vector o(k) and perturbed antenna weighting vectors w₊(k), w₋(k):

$o\left(k\right)=\{\begin{array}{cc}\frac{d-\left[w^H\left(k\right)d\right]w\left(k\right)}{\sqrt{1-{w^H\left(k\right)d}^2}},& w^H\left(k\right)d\ne 1\\ 0,& w^H\left(k\right)d=1\end{array}w_{+}\left(k\right)=\{\begin{array}{cc}\frac{w\left(k\right)+\mu o\left(k\right)}{\sqrt{1+{\mu }^2}},& w^H\left(k\right)d\ne 1\\ w\left(k\right),& w^H\left(k\right)d=1\end{array}w_{-}\left(k\right)=\{\begin{array}{cc}\frac{w\left(k\right)-\mu o\left(k\right)}{\sqrt{1+{\mu }^2}},& w^H\left(k\right)d\ne 1\\ w\left(k\right),& w^H\left(k\right)d=1\end{array}$

where μ is a real-valued step size, typically 0<μ≦1. Note that the second branch in each of the above equations corresponds to a case where the perturbation vector d is parallel to the antenna weight vector w(k), although the probability of such occurrence is quite small.

[0071] 5. Use the perturbed antenna weight vectors w₊(k), w₋(k) in place of the antenna weight vector w to transmit the first and second half-slots of the k-th slot, respectively. At the mobile station, the received power functions (or characteristics thereof) J(w₊(k)), J(w₋(k)) are measured and the sign bit:

$s\left(k\right)\stackrel{\Delta }{=}\mathrm{sign}\left\{J\left(w_{+}\left(k\right)\right)-J\left(w_{-}\left(k\right)\right)\right\},$

is fed back to the base station. In order to keep the adaptation from getting stuck at a local stationary point, albeit unlikely, the following convention is provided:

$\mathrm{sign}\left(a\right)\stackrel{\Delta }{=}\{\begin{array}{cc}1,& a\ge 0\\ 0,& a<0\end{array}$

which is slightly different from the common convention where the sign of zero is zero. Note that it is preferable that the mobile station also knows the weight vector w(k). In the deterministic perturbation method of B. Raghothaman, this is relatively straightforward to accomplish since the perturbation vector d(k) is selected from a deterministic set. However, it is more difficult to provide this information to both the base station and the mobile station and it may necessitate synchronization of the random generators at both ends of the link depending on the application.

[0072] 6. Update the antenna weight vector w according to:

$w\left(k+1\right)=\{\begin{array}{cc}\alpha w\left(k\right)+\mathrm{\alpha \mu }s\left(k\right)o\left(k\right),& w^H\left(k\right)d\ne 1\\ w\left(k\right),& w^H\left(k\right)d=1\end{array}\text{where:}\alpha \stackrel{\Delta }{=}\frac{1}{\sqrt{1+{\mu }^2}}.$

[0073] 7. Set slot index counter k←k+1 and loop back to step 3.

[0074] In accordance with the aforementioned method, FIG. 4 provides a two-dimensional diagram of an exemplary process of computing a tangential perturbation vector o(k) from a perturbation vector d and then computing the half-slot antenna weight vectors w₊(k), w₋(k). A dotted line (designated 405) represents the surface w^H w=1.

[0075] The properties of the constrained power function can be supported as hereinafter provided. The tangential perturbation gradient system is a time-recursive solution to the optimization problem:

$w_{\mathrm{opt}}=\text{arg}\underset{w}{\max }w^H\mathrm{Rw},$

subject to g(w)=0 where:

$g\left(w\right)\stackrel{\Delta }{=}1-w^Hw.$

Using the Lagrange multiplier method, the local maximizers w_i, i=1, 2, . . . , M, satisfy:

Rw_i=λ_iw_i.

Therefore, the λ_i's are the eigenvalues of the channel correlation matrix R and the w_i's are the corresponding eigenvectors thereof. Since the channel correlation matrix R is a nonnegative definite Hermitian matrix, λ_i≧0 for all i and:

R=WΛW^H,

where:

• • [0076] W=[w₁, w₂, . . . , w_M], and
  • [0077] Λ=diag(λ₁, λ₂, . . . , λ_M).
    It is customary to arrange the eigenvalues and the eigenvectors such that λ₁≧λ₂≧ . . . ≧λ_M. Furthermore, W satisfies W^H W=WW^H=I_M, where I_M is the identity matrix of size M. By defining u=W^H w, the power function can be rewritten as:

$C\left(u\right)\stackrel{\Delta }{=}u^H\Lambda u=J\left(w\right).$

[0078] In support of the foregoing, define:

Sg={u:g(u)=0}.

[0079] A proof of this lemma can be sketched as follows. First, note that u_i is a stationary point of the power function C(u) over the surface S_g. This is due to the fact that the directional derivative of the power function C(u) in the direction of v_i is zero, where v_i is any unit vector parallel to the hyperplane tangent to the constraint surface S_g at u=u_i. Indeed, since u_i=−∇_u^Hg(u) which is orthogonal to the level surface g(u)=0, then v^H_i u_i=0. Hence, the directional derivative of the power function C(u) in the direction of v_i at u=u_i is:

D_viC(u_i)=v_i^H∇_uHC(u_i)+∇_uC(u_i)v_i^Hλ_iu_i+u_i^Hλ_iv=0,

which establishes that u_i is a stationary point of the power function C(u) over the surface S_g. We now establish that u_i is neither a local maximum point nor a local minimum point of the power function C(u) over the surface S_g. Since the power function C(u) is analytic in u and continuous over the surface S_g, it is sufficient to show that there exist vectors e and f with arbitrarily small magnitudes such that:

g(u_i+e)=0,

g(u_i+f)=0,

C(u_i+e)>C(u_i), and

C(u_i+f)<C(u_i).

Such vectors indeed exist. For example, for any complex e₁, e₂, . . . , e_i−1, with e₁≠0, and for any complex f_i+1; f_i+2, . . . , f_M with f_M≠0, if:

$e=\frac{e_1u_1+e_2u_2+\dots +e_{i-1}u_{i-1}+u_i}{\sqrt{{e_1}^2+{e_2}^2+{e_{i-1}}^2+1}}-u_i,f=\frac{u_i+f_{i+1}u_{i+1}+f_{i+2}u_{i+2}+\dots +f_Mu_M}{\sqrt{1+{f_{i+1}}^2+{f_{i+2}}^2+\dots +{f_M}^2}}-u_i,$

then g(u_i+e)=0 and g(u_i+f)=0 hold. Note that ∥e∥→0 and ∥f∥→0 as e_k→0; k=1, 2, . . . , i−1 and f_l→0; l=i+1, i+2, . . . , M. Also,

$C\left(u_i+e\right)=\frac{{e_1}^2{\lambda }_1+{e_2}^2{\lambda }_2+\dots +{e_{i-1}}^2{\lambda }_{i-1}+{\lambda }_i}{{e_1}^2+{e_2}^2+\dots +{e_{i-1}}^2+1}>{\lambda }_i=C\left(u_i\right),C\left(u_i+f\right)=\frac{{\lambda }_i+{f_{i+1}}^2{\lambda }_{i+1}+{f_{i+2}}^2{\lambda }_{i+2}+\dots +{f_M}^2{\lambda }_M}{1+{f_{i+1}}^2+{f_{i+2}}^2+\dots +{f_M}^2},$

which satisfy C(u_i+e)>C(u_i) and C(u_i+f)<C(u_i), respectively.

[0080] The above lemma has a practical implication. Since the constrained power function C(u) has no local maximum in the surface S_g, the tangential perturbation gradient system converges to the global maximum. If the maximum eigenvalue of the channel correlation matrix R has multiplicity n, then u₁, u₂, . . . , u_n are the global maximum points of the power function C(u) in the surface S_g. However, how close the system and method can get to a global maximum point depends on the step size μ. A smaller step size μ in general yields a smaller steady-state error and a slower initial speed of convergence and vice versa. Thus, the step size μ should be selected to balance the convergence speed and the continuing antenna weight changes necessitated by changing transmit receive path dispersions.

[0081] The properties of the tangential perturbation gradient system and method will hereinafter be explored. Since the probability that the perturbation vector d is parallel to the antenna weight vector w(k) is very small, we explore the other cases for the our purposes of expediency. From the following relationship:

$o\left(k\right)=\frac{d-\left[w^H\left(k\right)d\right]w\left(k\right)}{\sqrt{1-{w^H\left(k\right)d}^2}},w^H\left(k\right)d\ne 1$

the tangential perturbation vector o(k) has a unit magnitude and is parallel to the hyperplane tangent to the constraint surface S_g, since w^H (k)o(k)=0. Therefore, at each step of the adaptation, the end of the antenna weight vector w(k) is moved by the same distance (i.e., the antenna weight vector w(k) is moved through a constant angular change). To see this quantitatively, the projection of weight vector w(k) onto w(k+1) is relatively constant for k since:

w^H(k)w(k+1)=α.

To see this another way:

∥w(k+1)−w(k)∥²=2(1−α).

This is a feature of the tangential perturbation gradient system that makes it converge faster than the perturbation methods of B. C. Banister, et al. and B. Raghothaman. For the perturbation methods, the projection of the antenna weight vector w(k) onto the antenna weight vector w(k+1) changes with k since it depends on the direction of the perturbation vector d at time k.

[0082] To see that the tangential perturbation gradient system has a gradient ascent behavior, the directional derivative of the power function J(w) in the direction of the tangential perturbation vector o(k) and evaluated at the antenna weight vector w=w(k) is:

$\begin{array}{c}{D}_{o\left(k\right)}J\left(w\left(k\right)\right)=o^H\left(k\right){\nabla }_{w^H}J\left(w\left(k\right)\right)+{\nabla }_wJ\left(w\left(k\right)\right)o\left(k\right)\\ =o^H\left(k\right)\mathrm{Rw}\left(k\right)+w^H\mathrm{Ro}\left(k\right)\\ =\frac{1+{\mu }^2}{2\mu }\left[J\left(w_{+}\left(k\right)\right)-J\left(w_{-}\left(k\right)\right)\right].\end{array}$

which together with:

$s\left(k\right)\stackrel{\Delta }{=}\mathrm{sign}\left\{J\left(w_{+}\left(k\right)\right)-J\left(w_{-}\left(k\right)\right)\right\},$

indicates that s(k) is the sign, and therefore a coarse estimate, of the directional derivative of the power function J(w) in the direction of the tangential perturbation vector o(k). Hence, the following relationship:

w(k+1)=αw(k)+αμs(k)o(k),|w^H(k)d|≠1,

can be rewritten as:

w(k+1)=αw(k)+αμ·sign{D_o(k)J(w(k))}o(k),

which can be viewed as a gradient-sign iteration subject to the constraint g(w)=0.

[0083] The step size and steady-state error will hereinafter be described. Since both the initial speed of convergence and the steady-state error tend to increase with the step size μ, it is sensible, especially in a static or slow-fading channel environment, to start with a large step size and decrease it monotonically over time until it reaches a sufficiently small value. To relate the step size to the steady-state error, the static channel case (constant R) is analyzed.

[0084] Suppose that at time k, the system reaches a global maximum so that the antenna weight vector w(k)=w₁. The system evolves between time k and k+1 by decreasing the power function J(w) from λ₁ to a smaller value. The worst case behavior is reviewed where power function J(w(k+i)) decreases at every time epoch from i=1 to i=p for some p≧1. In an actual operation, the value p is small (on the order of unity) since the probability that the power function J(w) decreases for the value p consecutive time steps decreases quickly with the value p.

[0085] Suppose the antenna weight vector w(k)=w₁ and let p≧1. The normalized power function J(w(k+p))/λ₁ is bounded below by:

$B_p\le \frac{1}{{\lambda }_1}J\left(w\left(k+p\right)\right),$

where the bound B_p is given by the following recursion. For i=1, 2, . . . , p−1,

$J_1={\alpha }^2,B_1={\alpha }^2,c_1=\alpha ,d_1=\mathrm{\alpha \mu },a_i=c_i^2,b_i=\sqrt{c_i^2-a_i^2},g_i=\frac{c_i\left(a_i-1\right)}{b_i},h_i=\frac{d_ia_i}{b_i},B_{i+1}={\alpha }^2\left(J_i+2\mu c_ig_i\right),c_{i+1}=\alpha \left(c_i+\mu g_i\right),d_{i+1}=\alpha \left(d_i+\mu h_i\right),J_{i+1}=c_{i+1}^2.$

[0086] $B_p\le \frac{1}{{\lambda }_1}J\left(w\left(k+p\right)\right),$

is a deterministic bound, it only represents a loose bound. An average bound is obtained by taking into account the distribution of the antenna weight vector w(k+i) in steady state.

[0087] We observe that the bound B_p for the normalized power function depends on the step size μ. Therefore, the step size μ value can be chosen so that the steady-state error stays below a certain level. FIG. 5 shows the bound B_p for the normalized power function as a function of the step size μ and for various values p. This bound explains quantitatively that the steady-state error increases with the size of the step size μ.

[0088] A graphical representation comparing the deterministic perturbation gradient approximation (denoted “DPGA”) to the tangential perturbation gradient system (denoted “T-DPGA”) is illustrated with respect to FIGS. 6A and 6B. The graphical representation includes the tangential perturbation gradient system without the tangential projection step with step sizes of 0.2 and 0.3 in FIGS. 6A and 6B, respectively. The illustrated graphical representation is provided for two transmit antennas, one receive antenna and 3, 10, and 30 kmph flat fading. An advantage of the tangential perturbation gradient system due to better tracking is visible at higher velocities.

[0089] Thus, a tangential perturbation gradient system and method for adaptive beamforming have been described. The tangential perturbation gradient system speeds up the convergence of the ordinary perturbation method. Additionally, the power function has no local maximum in the constraint set. Therefore, the tangential perturbation gradient system more readily converges to the global maximum. A lower bound on the power function has been developed which shows quantitatively that the steady-state error can increase with the adaptation step size, and based on this bound the step size can be selected to keep the relative steady-state error below a pre-set value. It should be noted, however, that this bound is relatively loose and therefore the step size selected according to this bound is provided for illustrative purposes only.

[0090] In view of the improvements realized with the deterministic perturbation gradient approximation, the principles provided thereby speed up the convergence associated therewith. Instead of adding the perturbation vector to the weight vector, a tangential component of the perturbation vector to the constraint surface (preferably representing a constrained power level) is constructed and the tangential component is used to update the weight vector. In accordance therewith, the tangential perturbation gradient system is a gradient-sign system, wherein the gradient in this case is the directional derivative of the cost function in the direction of the tangential perturbation vector.

[0091] Regarding beamforming in general, an attractive feature of beamforming is that when the number of resolvable paths is smaller than the number of transmit antennas, then beamforming outperforms space-time coding scheme, provided that the beamforming methodology converges to a global maximum as supported by B. C. Banister, et al. The price paid for this gain in performance is the fact that beamforming employs downlink channel knowledge or a feedback bandwidth. Space-time coding, on the other hand, is typically a blind methodology wherein channel knowledge is not assumed at the transmitter.

[0092] Since feedback information is involved between the base station and the mobile station, a protocol is established in order for the network to recognize the information. In general, a beamformer method should possess a simple mechanism to make a tradeoff between performance (including speed of convergence and tracking), feedback rate and computational complexity. The question to address is how to devise a beamforming technique that offers the “best” performance for any given amount of feedback rate.

[0093] Finite feedback information typically represents a quantized version of a vector quantity. The concept of vector quantization has been addressed in the beamforming literature. For example, a beamforming vector may be quantized by optimizing a beamformer criterion (typically the received power) over a pre-designed finite codebook and the index of the vector that optimizes the criterion is fed back to the transmitter. (See, e.g., A. Narula, M. J. Lopez, M. D. Trott, and G. W. Wornell (“A. Nurala, et al.”), “Efficient Use of Side Information in Multiple-Antenna Data Transmission over Fading Channels,” IEEE J. Select. Areas in Comm., vol. 16, pp. 1423-1436, October 1998, K. K. Mukkavilli, A. Sabharwal, E. Erkip, and B. Aazhang (“K. K. Mukkavilli, et al.”), “On Beamforming with Finite Rate Feedback in Multiple-Antenna Systems,” IEEE Trans. Info. Theory, vol. 49, pp. 2562-2579, October 2003, and D. J. Love, R. W. Heath Jr., and T. Strohmer (“D. J. Love, et al.”), “Grassmannian Beamforming for Multiple-Input Multiple-Output Wireless Systems,” IEEE Trans. Info. Theory, vol. 49, pp. 2735-2747, October 2003, all of which are incorporated herein by reference). Instead of the delivered power, the average data rate, though less feasible, can be optimized subject to the bit error rate (“BER”) and transmit power constraints. (See, e.g., P. Xia, S. Zhou, and G. B. Giannakis, “Multiantenna Adaptive Modulation with Beamforming Based on Bandwidth-Constrained Feedback,” IEEE Trans. Comm., vol. 53, pp. 256-536, March 2005, which is incorporated herein by reference).

[0094] Codebook-based selection, while representing a nice and simple idea wherein a codebook element selected in one slot has little or no algorithmic dependence on the previous one has several drawbacks. In order for the beamformer to be close to a better-quality solution, the codebook size needs to be sufficiently large. A larger codebook size, however, employs a higher receiver computational complexity. In addition, a codebook search is inherently a nonadaptive technique and therefore does not naturally converge to a global maximizer.

[0095] A beamforming system and method is now described in an embodiment that is adaptive and further improves global convergence. Convergence speed is improved by selecting a better increment vector. A quantized version of the increment vector is fed back to the transmitter. The beamforming system and method advantageously is very robust with respect to a quantization error. In particular, for a very coarse quantization scheme wherein only the sign of each component is taken, the loss in convergence speed can be small and the directional derivative (constrained gradient) of the objective function is nonnegative everywhere. In addition, when the feedback information is represented by only one bit, global convergence is still assured. According to the aforementioned features, the beamforming system and method provide global convergence regardless of the quantization error. A communication system including these elements is described in the paper by H. Nguyen and B. Raghothaman, entitled “Quantized-Feedback Optimal Adaptive Beamforming for FDD Systems,” 2006 IEEE International Conference on Communications, Volume 9, June 2006, pp. 4202-4207, which is incorporated herein by reference.

[0096] The signaling model described above is again considered, including a base station with M transmit antennas and a mobile station (“a receiving station”) with N receive antennas. The variables x(n), σ_x², y_r(n), w, J(w), R, and H_r, are as previously defined. As before, in addition to the information sequence x(n), each transmit antenna also carries a pilot sequence covered by a predetermined spreading sequence, the channel impulse response for each transmit-receive antenna pair is estimated from the associated pilot, the time dependency of the channel matrix H_r has been suppressed, and the channel is considered to be block-wise constant.

[0097] Regarding beamforming and a receiver, the beamformer (i.e., setting the complex transmit antenna weights), can be computed in an embodiment of a beamforming system and method such that a received signal power J(w) is further improved. In the case of flat fading, where the delay D=0, it is well known that improving the received signal power J(w) is equivalent to improving the effective signal-to-noise ratio (“SNR”), and hence the channel capacity with the use of maximal ratio combining (“MRC”). (See, e.g., P. A. Dighe, R. K. Mallik, and S. S. Jamuar, “Analysis of Transmitreceive Diversity In Rayleigh Fading,” IEEE Trans. Comm., vol. 51, pp. 694-703, April 2003, and J. B. Andersen, “Antenna Arrays in Mobile Communications: Gain, Diversity, and Channel Capacity,” IEEE Antennas and Propag. Mag., vol. 42, pp. 12-16, April 2000, which are incorporated herein by reference.)

[0098] In this case, the noisy received signal is:

$z\left(n\right)\stackrel{\Delta }{=}y\left(n\right)+v\left(n\right)=\mathrm{Hwx}\left(n\right)+v\left(n\right)$

where:

$H\stackrel{\Delta }{=}\left[\begin{array}{cccc}{h}_{1,1}& h_{1,2}& \cdots & h_{1,M}\\ h_{2,1}& h_{2,2}& \cdots & h_{2,M}\\ ⋮& ⋮& \cdots & ⋮\\ h_{N,1}& h_{N,2}& \cdots & h_{N,M}\end{array}\right],\mathrm{and}$ $v\left(n\right)~\mathrm{CN}\left(0,N_oI_N\right)$

model a circular complex white Gaussian noise process of spectral density N_o. Here, I_N denotes an N×N identity matrix. With MRC, a combiner c is designed such that for any given antenna weight vector w, the post-combination SNR experienced by an estimate of the transmitted information signal:

{circumflex over (x)}(n)=c^Hz(n)=c^HHwx(n)+c^Hv(n)

is maximized. This SNR is:

$\eta \left(c,w\right)\stackrel{\Delta }{=}\frac{E\left[{c^H\mathrm{Hwx}\left(n\right)}^2\right]}{E\left[{c^Hv\left(n\right)}^2\right]}=\frac{{\sigma }_x^2}{N_o}\frac{c^H\left[{\mathrm{Hww}}^HH^H\right]c}{c^Hc}.$

For any given antenna weight vector w, the SNR η(c, w) is maximized when the combiner c is proportional to the principal eigenvector of the rank-1 matrix Hww^H H^H, i.e., when:

$c=c_{\max }\stackrel{\Delta }{=}\gamma \mathrm{Hw}$

for any scalar γ≠0. Then,

$\eta \left(\gamma \mathrm{Hw},w\right)=\frac{{\sigma }_x^2}{N_o}w^HH^H{\mathrm{Hw}}^H=\frac{{\sigma }_x^2}{N_o}w^H\mathrm{Rw}=\frac{{\sigma }_x^2}{N_o}J\left(w\right).$

[0099] Thus, with MRC, maximizing the effective SNR η over the antenna weight vector w is equivalent to maximizing the received signal power J(w), thus maximizing the Shannon channel capacity as observed by J. B. Andersen, introduced above. This forms the basis for employing beamforming as a technique to enhance the channel capacity. When the received signal power J(w) is maximized subject only to the constraint w^H w=1, the transmitter achieves maximal ratio transmission (“MRT”), as proposed by T. K. Y. Lo, which provides the maximum channel capacity. (See, e.g., T. K. Y. Lo, “Maximum Ratio Transmission,” in Proc. IEEE Int. Comm. Conf., vol. 2, (Vancouver, BC), pp. 1310-1314, Jun. 6-10, 1999, and T. K. Y. Lo, “Maximum Ratio Transmission,” IEEE Trans. Comm., vol. 47, pp. 1458-1461, October 1999, which are incorporated herein by reference).

[0100] The practical issue arising in FDD systems where the channel is unknown at the transmitter leads to proposals to feed back a quantized version of the channel information (see reference by A. Narula, et al. introduced above) or of the beamformer (see references by K. K. Mukkavilli, et al. and D. J Love, et al. introduced above), encompassing antenna selection diversity (see references by N. R. Sollenberger and A. Wittneben introduced below) as special cases. (See, e.g., N. R. Sollenberger, “Diversity and Automatic Link Transfer for a TDMA Wireless Access Link,” in Proc. IEEE Global Telecom. Conf., vol. 1, (Houston, Tex.), pp. 532-536, Nov. 29-Dec. 2, 1993, and A. Wittneben, “Analysis and Comparison of Optimal Predictive Transmitter Selection and Combining Diversity for DECT,” in Proc. IEEE Global Telecom. Conf., vol. 2, pp. 1527-1531, Nov. 13-17, 1995, which are incorporated herein by reference.) With respect to the present system and method, the beamformer and the channel information is not quantized. Instead, a selected (e.g., optimal) increment vector is quantized such that the beamformer retains the ability to converge to the MRT beamformer.

[0101] The case of flat fading is of special interest since it models a large class of practical wireless links. Flat fading can be used to model ordinary narrow-band channels where the signal bandwidth is small relative to the carrier frequency. Flat fading can also be employed to model each bin of a wide-band orthogonal frequency division multiplexing (“OFDM”) system since each bin can be treated as a narrow-band signal. For a general frequency selective channel wherein the delay (D>0), maximizing the received signal power J(w) optimizes the received SNR, equal to

$\frac{J\left(w\right)}{N_o},$

but does not necessarily optimize the channel capacity. In such a case, the optimal-capacity receiver employs an equalizer in which the antenna weight vector w is employed in a more complicated manner than for the MRC receiver, a linear transformation of the antenna weight vector w in:

$c=c_{\max }\stackrel{\Delta }{=}\gamma \mathrm{Hw}.$

[0102] Referring now to FIG. 7, illustrated is a block diagram of a communication system including an embodiment of a quantized-feedback optimal adaptive (“QFOA”) beamforming system. The communication system includes a base station 300 and a receiving station (e.g., mobile station 370). The communication channels are defined by radio links such as forward-link channels 305 and reverse-link channels 310. Information sent to the mobile station 370 is communicated by the base station 300 over the forward-link channels 305 and information originated at the mobile station 370 for communication to the base station 300 is communicated over reverse-link channels 310.

[0103] The communication system may be a cellular communication system constructed pursuant to any of a number of different cellular communication standards. For instance, the base station and mobile station may be operable in a code division multiple access (“CDMA”) communication system such as a third generation (“3G”) CDMA communication that provides for 1 xEV-DV (“EVolution, Data and Voice”) data and voice communications. Those skilled in the art, however, realize that the QFOA beamforming system described herein are employable in any number of communication systems including other types of cellular communication systems such as a global system for mobile (“GSM”) communications systems that provides for general packet radio service or enhanced data for GSM evolution data services, each of which also provides for data communications. The communication system is also representative of other types of radio and other communication systems in which data is communicated over channels that are susceptible to distortion caused by fading or other conditions. Those skilled in the art should understand that the principles described herein are operable in any communication system employing closed-loop transmit diversity.

[0104] The base station 300 forms part of a radio access network that also includes a radio network controller (“RNC”) 315 coupled to a gateway 320 and a mobile switching center (“MSC”) 325. The gateway 320 is coupled to a packet data network (“PDN”) 330 such as the Internet backbone and the mobile switching center 325 is coupled to a public switched telephone network (“PSTN”) 335. A correspondent node (“CN”) 337 is coupled to the packet data network 330 and to the PSTN 335. The correspondent node 337 represents a data source or a data destination from which, or to which, information is routed during operation of the communication system.

[0105] The base station 300 includes a receiver 340 and a transmitter 345. A forward-link signal to be communicated by the base station 300 to the mobile station 370 is converted into a format for communication over the forward-link channels 305 by the transmitter 345. Closed-loop feedback information is returned by the mobile station 370 to the base station 300 by way of the reverse-link channels 310. The mobile station 370 also includes a receiver 375 and a transmitter 380. The receiver 375 (which may include subsystems such as a receive filter and an equalizer) operates to receive, and operate upon, the forward-link signals transmitted by the base station 300 over the forward-link channels 305, and the transmitter 380 operates to transmit reverse-link signals over the reverse-link channels 310 to the base station 300.

[0106] The base station 300 and the mobile station 370 may include multiple antennas, and the base station 300 and mobile station 370 combination forms a multiple-input, multiple-output (“MIMO”) system. For purposes of clarity, the base station 300 includes M base station antennas designated 347-1 to 347-M (hereinafter referenced as base station antennas 347). Also for purposes of clarity, the mobile station 370 includes N mobile station antennas designated 385-1 to 385-N (hereinafter referenced as mobile station antennas 385). Those skilled in the art should understand, however, that any number of antennas (e.g., one receive antenna) may be employed in accordance with the principles of the present invention.

[0107] The base station transmitter 345 includes an encoder 350 that encodes data to form encoded data. The encoded data is provided [via a transmit filter (not shown)] to an up-mixer 355 with an up-mixing constant v(t) to generate an up-mixed signal. The up-mixed signal is provided via weighting elements (two of which are referenced and designated as first and second weighting elements 360, 362, respectively) on separate branches to ones of the base station antennas 347. Once the up-mixed signals are weighted, the weighted signals are applied to the base station antennas 347 for transmission to the mobile station 370. Of course, other operations may be performed on the weighted signals prior to transmission to the mobile station 370.

[0108] The base station 300 also includes a QFOA beamforming system that adjusts the values of the weightings applied to the first and second weighting elements 360, 362 in a manner that enhances antenna weighting selection pursuant to a closed-loop transmit diversity system. The QFOA beamforming system (which may be embodied in hardware, software, or combinations thereof) includes a beamformer selector (“BS”) 365 that operates to determine an updated direction or increment vector based on a quantized increment vector (received by the receiver 340 in the base station 300) from the mobile station 370. For instance, the beamformer selector 365 may re-orthogonalize the quantized increment vector. As will become more apparent below, the quantized increment vector may be projected onto a hyperplane tangent to a constraint hypersurface thereof (e.g., re-orthogonalizes).

[0109] The QFOA beamforming system also includes a weight vector modifier (“WEM”) 367 that modifies a tangential component of the weight vectors selected by the beamformer selector 367, which are provided to a vector applicator (“VA”) 369 for application to the first and second weighting elements 360, 362. For instance, the weight vector modifier 367 calculates an antenna weight vector by adding an increment (e.g., of a magnitude of less than unit length) to a previous antenna weight vector proportional to the re-orthogonalized quantized increment vector and renormalizes the antenna weight vector to unit length (“unit magnitude”). Since the increment vector points in a direction that yields a sufficient gradient of the objective function subject to the constraint, the convergence speed is improved. The weightings of the first and second weighting elements 360, 362 determine the values of the signals transmitted by the base stations antennas 347. The forward-link signals generated on the forward-link channels 305 are delivered to the mobile station 370.

[0110] The mobile station 370 includes a detector 390 (which may include subsystems such as a combiner and may be a subsystem of the receiver 375) that detects and measures characteristics (e.g., representing power levels) of the forward-link signals (in accordance with the pilot signals thereof) transmitted by the base station 300. For instance, the detector 390 may measure a downlink channel correlation matrix for the base station antennas 347 from pilot signals. The detector 390 may measure the downlink channel correlation matrix from a discrete-time channel impulse response between the base station antennas 347 and the mobile station antenna(s) 385. More specifically, the detector 390 adapts a transmit vector by computing a direction of preferable adaptation and feeds the quantized increment vector (e.g., one bit) back to the base station 300.

[0111] The detector 390 may compute an increment vector normal to a previous antenna weight vector rendering positive (e.g., maximize) a directional derivative of total received power from the base station antennas 347, and quantize the increment vector component by component to produce a quantized increment vector (e.g., via a uniform quantizer). The quantized increment vector is thereafter employed by the base station 300 to adjust the weightings of the first and second weighting elements 360, 362 for the base station antennas 347 to refine the forward-link signals. Of course, the quantized increment vector may adjust the weightings of any number of weighting elements depending on the number of base station antennas. The QFOA beamforming system is an adaptive system and the aforementioned steps may be repeated several times to adapt the transmit antenna weights towards an optimal or preferable solution depending on the communication system and application.

[0112] Turning now to FIG. 8, illustrated is a functional block diagram of an embodiment of a QFOA beamforming system. While the support for the functional blocks will be described in more detail below, an outline of the respective functions will hereinafter be provided. Beginning with the sending station, a beamforming selector 410 determines an updated direction or increment vector based on a quantized increment vector received from a receiving station. The quantized increment vector may be projected onto a hyperplane tangent to a constraint hypersurface thereof. A weight vector modifier 420 modifies a tangential component of the weight vectors selected by the beamformer selector 410, which are provided to a vector applicator 430 for application of the weight vectors. Regarding the receiving station, a detector 440 adapts a transmit vector by computing a direction of preferable adaptation and feeds a quantized increment vector back to the sending station.

[0113] With respect to the beamforming system and method, due to the transmit power limitation, the beamformer should satisfy the scalar constraint g(w)=0, where the constraint function g(w) is defined as:

$g\left(w\right)\stackrel{\Delta }{=}1-w^Hw.$

An objective is to develop an adaptive solution for the maximization problem:

$w\max \stackrel{\Delta }{=}\text{arg}\underset{w\in S_g}{\max }J\left(w\right),$

where:

$S_g\stackrel{\Delta }{=}\left\{w\text{:}g\left(w\right)=0\right\}.$

[0114] Suppose that the antenna weight vector w(k) denotes a solution at the k-th step of the adaptation. The time variable k also indexes the “slots” of a transmission wherein the duration of each slot is on the order of the channel coherence time sufficiently long for all channel estimation and pilot tracking purposes. This adaptation is governed by two conditions, one of which is:

w(k)εS_g∀k.

The other condition is that the antenna weight vector w(k+1) should be related to the antenna weight vector w(k) in such a way that the received signal power J(w) has an “optimal” increase from the antenna weight vector w=w(k) to the antenna weight vector w=w(k+1). In the unconstrained optimization problem, it is well known that the antenna weight vector w(k+1) can be obtained by adding to the antenna weight vector w(k) a scaled version of the gradient of the received signal power J(w). In other words, for some constant step size μ, the antenna weight vector is w(k+1)=w(k)+μ∇ _wH J(w(k)), commonly known as the steepest gradient ascent algorithm. The presence of the constraint w(k)εS_g ∀k renders the direct use of the steepest gradient ascent algorithm principally inapplicable. Indeed, the antenna weight vector w(k+1) could be normalized at each step, but apparently this process has no mechanism to prevent the received signal power J(w) from decreasing between time k and time k+1.

[0115] To proceed, the concept of directional derivative is employed for a real-valued scalar function. For any real-valued vector Θ ε R^2M (which denotes a Euclidean real vector space with 2M dimensions) and a real-valued scalar function ƒ(Θ), the directional derivative of ƒ(Θ) in the direction of a real-valued unit vector v ε R^2M is given by:

D_vƒ(Θ)=∇_Θƒ(Θ)v.

[0116] Extending this result to a real-valued scalar function with complex input arguments, the directional derivative of any real-valued scalar received signal power J(w) in the direction of a complex unit vector o ε C^M (which denotes a Euclidean complex vector space with M dimensions) is given by:

D_oJ(w)=o^H∇_wHJ(w)+∇_wJ(w)o,

referred to as EQN A. It should be noted by convention, ∇ _wJ(w) is a row vector and ∇ _wH J(w) is a column vector. By extending the real-valued scalar function with complex input arguments, the received signal power J(w)=ƒ(Θ) has been expressed as a function of the real-valued vector:

Θ=[w_Re^T,w_Im^T]^T,

wherein the quantities subscripted by R_e and I_m denote the real and imaginary parts of the associated quantity, respectively. Then, D_oJ(w) is defined as the directional derivative of ƒ(Θ) in the direction of:

v=[o_R^T,o_I^T]^T.

That is:

$D_oJ\left(w\right)\stackrel{\Delta }{=}D_vf\left(\Theta \right),$

referred to as EQN B.

[0117] The result provided by EQN A follows from EQN B by employing the Wirtinger complex calculus (see, P. Henrici, “Applied and Computational Complex Analysis,” vol. III, pp. 287-288, New York, N.Y.: Wiley & Sons, 1986, which is incorporated herein by reference), wherein the operator identities:

$\frac{\partial }{\partial w}=\frac{1}{2}\left(\frac{\partial }{\partial w_R}-j\frac{\partial }{\partial w_I}\right),\mathrm{and}\frac{\partial }{\partial w^{*}}=\frac{1}{2}\left(\frac{\partial }{\partial w_R}+j\frac{\partial }{\partial w_I}\right)$

yield results consistent with separate differentiation regarding the real and imaginary parts and allow the treatment of the antenna weight vectors w and w* as independent variables, i.e.,

$\frac{\partial w}{\partial w}=I,\mathrm{and}$ $\frac{\partial w}{\partial w^{*}}=0.$

The term S_g is the closed set that defines a constraint surface with a unit-radius hypersphere centered at the origin. Therefore, if the tail of the antenna weight vector w(k) is held at the origin of C^M, then as the time k→∞, the head of the antenna weight vector w(k) leaves a series of “headprints” on the surface of the hypersphere. The antenna weight vector w(k+1) is obtained by pulling the head of the antenna weight vector w(k) to a close neighboring point on the surface of the hypersphere in a direction tangent to an equal-level surface (a constraint function) g(w)=0. Then, the direction in which the pull results in the steepest rate of increase in the received signal power J(w) is some increment vector o(k) that is tangential to the constraint surface S_g when the antenna weight vector w=w(k) and results in the largest directional derivative, i.e.,

$o\left(k\right)=\text{arg}\underset{o}{\max }D_oJ\left(w\left(k\right)\right)=\text{arg}\underset{o}{\max }\left[o^H\mathrm{Rw}\left(k\right)+w^H\left(k\right)\mathrm{Ro}\right],$

subject to:

o^Hw(k)=0, referred to as constraint A, and

1−o^Ho=0, referred to as constraint B.

[0118] The constraint A provides that the increment vector o is tangent to the equal-level surface g(w)=0 when the antenna weight vector w=w(k). To see this, the increment vector o should be orthogonal to the gradient of the equal-level surface g(w), that is 0=o^H ∇ _wHg(w(k))=−o^H w(k), resulting in constraint A. The constraint B is the usual condition for the unit increment vector involved in the definition of directional derivative. Thus, the increment vector o(k) can be obtained by solving for increment vector o in the Lagrange equation:

Rw(k)=l₁w(k)−l₂o,

subject to the constraints A and B, where l₁ and l₂ are the Lagrange multipliers. If the antenna weight vector w(k) is not an eigenvector of the channel correlation matrix R, then the increment vector is given by:

$o\left(k\right)=\frac{1}{l_2}\left[R-\left(w^H\left(k\right)\mathrm{Rw}\left(k\right)\right)I_M\right]w\left(k\right)$

referred to as EQN C, where:

l₂=√{square root over (w^H(k)R²w(k)−[w^H(k)Rw(k)]²)}{square root over (w^H(k)R²w(k)−[w^H(k)Rw(k)]²)}{square root over (w^H(k)R²w(k)−[w^H(k)Rw(k)]²)}{square root over (w^H(k)R²w(k)−[w^H(k)Rw(k)]²)},

referred to as RELN A.

[0119] The case where the iterative antenna weight vector w(k) is an eigenvector of the channel correlation matrix R, though very unlikely to occur, will hereinafter be addressed. It should be noted that the result given by EQN C is available at the receiver. Inasmuch as the receiver is a forward-link receiver, the parameters to calculate the increment vector o(k) described in EQN C are known or can be determined thereby. In order to make this information partially available at the transmitter, a quantized version of the increment vector o(k) is fed back to the transmitter via a feedback channel. Of course, several quantization schemes are possible. A good quantization method is to simply take the sign of the real and imaginary part of each vector component separately, yielding the quantized vector:

$\stackrel{\_}{o}\left(k\right)=\frac{s_R\left(k\right)+js_I\left(k\right)}{\sqrt{2M}},$

referred to as EQN D, where:

$s_R\stackrel{\Delta }{=}\mathrm{sign}\left(o_R\left(k\right)\right)$ $s_I\left(k\right)\stackrel{\Delta }{=}\mathrm{sign}\left(o_I\left(k\right)\right).$

Note that sign(·) returns a vector whose components are the signs of the individual components of the real-valued vector argument so that each component of sign(x) is either +1 or −1 for any real-valued vector x. Here, as described above, we again use the convention sign(0)=1.

[0120] The above quantization can be generalized to a denser quantization where b bits are used to represent the real or imaginary parts of each component of the increment vector o(k). The quantization set is defined as:

$Q\stackrel{\Delta }{=}\left\{-q_{2^{b-1}-1},\dots ,-q_1,-q_0,q_0,q_1,\dots ,q_{2^{b-1}-1}\right\}$

which consists of 2^b points on the real-number axis and symmetric about the origin. Without loss of generality, it is assumed that:

q_m<q_n, ∀m<n.

Since the quantization set Q is used to quantize a number in the closed interval [−1, 1], the following constraint is imposed:

0<q_2b−1−1≦1.

If the following quantization levels are used:

$q_i=\frac{2i+1}{2^b-1},i=0,1,\dots ,2^{b-1}-1,$

referred to as RELN B, then the quantization set Q consists of 2^b uniformly spaced points between and including −1 and +1, resulting in a uniform quantization. However, it is perhaps more preferable to design the quantization levels q_i as a function of i such that the quantization set Q has a higher concentration toward zero, i.e., q_i+1−q_i≧q_i−q_i−1, to match the distribution of the real and imaginary parts of the components of the increment vector o(k).

[0121] The quantization of the increment vector (referred to as a quantized increment vector) o(k) is given by:

$\stackrel{\_}{o}\left(k\right)=\frac{q\left(o_R\left(k\right)\right)+jq\left(o_I\left(k\right)\right)}{q\left(o_R\left(k\right)\right)+jq\left(o_I\left(k\right)\right)},$

referred to as EQN E, where the quantization level q(x) is a component-wise quantizer and has the same dimensions as x for any real-valued vector x. The l-th element of the quantization level q(x) is obtained by quantizing the l-th element of x to the closest element in the quantization set Q, i.e.,

${\left\{q\left(x\right)\right\}}_I=\text{arg}\underset{v\in Q}{\min }x_I-v,$

where x_l is the l-th element of x. When b=1 and with uniform distribution as represented by RELN B, the quantization of EQN E reduces to the sign quantization of EQN D.

[0122] The above quantization scheme can be further generalized. The real and/or imaginary part of one or more components of the increment vector o(k) can be quantized to its sign, or to any value that has its sign. In particular, if either the real or imaginary part of only one component of the increment vector o(k) is quantized to its sign, a one-bit feedback scheme is achieved. The fact that the QFOA beamforming system provides this widely flexible quantization scheme represents an advantage over other presently available systems.

[0123] The information that is fed back to the transmitter is the bits that represent the quantized increment vector ō(k). At this point, the transmitter and the receiver both know the quantized increment vector ō(k) and the antenna weight vector w(k). However, unlike the increment vector o(k), the quantized version ō(k) thereof may no longer be orthogonal to the antenna weight vector w(k). Nonetheless, at both the transmitter and the receiver, the following may be obtained:

$\tilde{o}\left(k\right)=\frac{\stackrel{\_}{o}\left(k\right)-\left[w^H\left(k\right)\stackrel{\_}{o}\left(k\right)\right]w\left(k\right)}{\sqrt{1-{w^H\left(k\right)\stackrel{\_}{o}\left(k\right)}^2}},$

referred to as EQN F, which is a unit vector re-orthogonalized to the antenna weight vector w(k) and approximates the direction of the increment vector o(k). As described below, the above quantization schemes (including the case of 1-bit feedback) will satisfy global convergence.

[0124] For completeness, the case where the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R will hereinafter be addressed. In this case, REL A gives l₂=0 and EQN C yields an indeterminate vector of the form

$\frac{0}{0}.$

However, from:

$o\left(k\right)=\text{arg}\underset{o}{\max }D_oJ\left(w\left(k\right)\right)=\text{arg}\underset{o}{\max }\left[o^H\mathrm{Rw}\left(k\right)+w^H\left(k\right)\mathrm{Ro}\right],$

if the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R, then the directional derivative is zero in any direction tangent to the equal-level surface g(w)=0 when the antenna weight vector w=w(k) since this direction is orthogonal to the antenna weight vector w(k). Therefore, any unit increment vector o(k) orthogonal to the antenna weight vector w(k) is acceptable to pull the antenna weight vector w(k+1) away from any local stationary point. There are infinitely many such vectors and herein are listed several thereof. Let 1≦m<n≦M such that the antenna weight vectors w_m(k)≠0 and w_n(k)≠0, wherein the antenna weight vector w_i(k) denotes the i-th component of the antenna weight vector w(k). Then,

$o\left(k\right)=\frac{{\left[\begin{array}{c}{o}_1\left(k\right),\dots ,o_{m-1}\left(k\right),-w_n\left(k\right),o_{m+1}\left(k\right),\dots ,\\ o_{n-1}\left(k\right),w_m\left(k\right),o_{n+1}\left(k\right),\dots ,o_M\left(k\right)\end{array}\right]}^H}{\sqrt{{w_m\left(k\right)}^2+{w_n\left(k\right)}^2}},$

referred to as EQN G, is an acceptable candidate, wherein the increment vector o_i(k)=0 for i≠m, n. Note that the antenna weight vector −w_n(k) is placed at the m-th position and the antenna weight vector w_m(k) is put at the n-th position. If M is even, then:

o(k)=[−w₂(k),w₁(k),−w₄(k),w₃(k), . . . ,−w_M(k),w_M−1(k)]^H,

referred to as EQN H is another acceptable candidate. After the selection of the increment vector o(k), the quantization process above can be employed to obtain the increment vector õ(k).

[0125] Once the increment vector õ(k) is available, the updated beamformer is given by:

w(k+1)=αw(k)+αμõ(k),

referred to as EQN I, wherein μ ε (0, 1] represents the step size and:

$\alpha =\frac{1}{\sqrt{1+{\mu }^2}}.$

[0126] Thus, the QFOA beamforming system and method are summarized with respect to the flow diagram of FIG. 9 and with a base station with M transmit antennas and mobile station with N receive antennas as set forth below.

[0127] 1. Generate an arbitrary initial base station antenna weight vector w(1) with ∥w(1)∥=1 according to a pre-determined procedure. An equal gain beamformer is a recommendable candidate.

[0128] 2. Set the time k=1.

[0129] 3. Use the “previous” antenna weight vector w(k) to transmit the k-th time slot. At the mobile station, the channel correlation matrix R is measured from pilot signals, e.g., using the discrete-time channel impulse response between transmit antennas and receive antenna(s).

[0130] 4a. If the antenna weight vector w(k) is not an eigenvector of the channel correlation matrix R, the mobile station computes the antenna weight increment vector o(k) normal to a previous antenna weight vector according to EQN C and RELN A. The antenna weight increment vector o(k) maximizes, or in an alternative embodiment, renders positive a directional derivative of total received power at the receiving antenna from the multiple antennas of the base station—under the constraints that antenna weight increment vector o(k) is of unit length and is tangent to the equal-level surface g(w)=0 at w=w(k), i.e., is normal to the antenna weight vector w(k). As will be clarified later, the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R if l₂=0 in RELN A. If l₂=0, then the antenna weight increment vector o(k) can be selected as any vector orthogonal to antenna weight vector w(k), several candidates of which are listed in EQNs G and H.

[0131] 4b. A quantized version ō(k) of the antenna weight increment vector o(k) is obtained according to EQN E, or according to EQN D, if b=1, where b is the number of bits. The quantization of the antenna weight increment vector o(k) is performed component by component. In a preferred embodiment, a uniform quantizer is employed. Alternatively, a nonuniform quantizer is employed.

[0132] 5. The bits that represent the quantized increment vector ō(k) are sent back to the base station. At this point, the base station and the mobile station both know the quantized antenna weight increment vector ō(k) and the antenna weight vector w(k).

[0133] 6a. The base station and the mobile station “re-orthogonalize” the quantized increment vector ō(k) to the antenna weight vector w(k) to produce the re-orthogonalized antenna weight increment vector õ(k) of unit length according to EQN F.

[0134] 6b. A new (i.e., of unit magnitude, w^H w=1) antenna weight vector w(k+1) is computed from the antenna weight vector w(k) by the base station and the mobile station according to EQN I using a step size μ. The new normalized antenna weight vector is computed by adding the step size μ times the re-orthogonalized quantized antenna weight increment vector õ(k) to the antenna weight vector, i.e., an increment that is proportional to the re-orthogonalized quantized antenna weight increment vector is added to the antenna weight vector. The resulting new antenna weight vector is scaled by a normalizing factor so that it is of unit magnitude. The step size μ is preferably selected in the range 0<μ≦1, i.e., the length of the increment added to the antenna weight vector w(k) is positive but preferably less than or equal to unity. In a preferred embodiment, the step size μ is a constant which can be readily determined by simulation or by experiment in view of the application. Exemplary values of the step size μ are 0.2 and 0.5.

[0135] 7. Then, increment the time k to k+1 and loop back to step (3) to repeat the procedure for the next slot.

[0136] The QFOA beamforming methodology as described herein is a time-recursive solution to an optimization problem:

$w_{\mathrm{opt}}=\text{arg}\underset{w}{\max }w^H\mathrm{Rw},$

subject to the equal-level surface g(w)=0. Using a Lagrange multiplier method, the local maximizers w_i, i=1, 2, . . . , M, satisfy:

Rw_i=λ_iw_i.

Therefore, the λ_i's are the eigenvalues of the channel correlation matrix R and the w_i's are the corresponding eigenvectors of the channel correlation matrix R. Since channel correlation matrix R is a nonnegative definite Hermitian matrix, then:

λ_i≧0 for all i, and

R=WΛW^H,

where:

• • [0137] W=[w₁, w₂, . . . , W_M], and
  • [0138] Λ=diag(λ₁, λ₂, . . . , λ_M).

[0139] In the following, several properties of the methodology are examined that provide global convergence. That is, the time k→∞ and the antenna weight vector w(k)→w_max where w_max is an eigenvector of the channel correlation matrix R belonging to the maximum eigenvalue λ₁ and the received power signal J(w(k))→λ₁.

[0140] Lemma 1: For any antenna weight vector w(k), there holds:

D_o(k)J(w(k))≧0,

referred to as RELN C. The equality holds if the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R. By proof:

$\begin{array}{c}{D}_{o\left(k\right)}J\left(w\left(k\right)\right)=\frac{2}{l_2}\left\{w^H\left(k\right)R^2w\left(k\right)-{\left[w^H\left(k\right)\mathrm{Rw}\left(k\right)\right]}^2\right\}\\ =2l_2\\ =2\left[R-\left(w^H\left(k\right)\mathrm{Rw}\left(k\right)\right)I_M\right]w\left(k\right)\end{array}$

[0141] The increment vector that directly affects the beamformer update is increment vector õ(k) (see EQN F). Since the increment vector õ(k) is obtained via a quantization process, a question arises as to whether the directional derivative of the received signal power J(w) in the direction of increment vector õ(k) is still nonnegative as the directional derivative in the direction of the increment vector o(k). This question is answered in the following lemma, which indicates that the inequality D_o(k)J(w(k))≧0 still holds with the increment vector o(k) replaced by the increment vector õ(k).

[0142] Lemma 2: For any antenna weight vector w(k), the directional derivative of the received signal power J(w) in the direction of the increment vector õ(k) is nonnegative, i.e.:

D_õ(k)J(w(k))≧0.

[0143]
x^Tq(x)≧0, for x=0,

wherein the quantization level q(x) is such that sign({q(x)}_l)=sign(x_l). The quantization schemes described above satisfy the aforementioned property. For the case where the antenna weight vector w(k) is not an eigenvector of the channel correlation matrix R, the following are defined as set forth below:

$o_R\stackrel{\Delta }{=}\Re \left\{o\left(k\right)\right\}=\frac{1}{l_2}\left[R_Rw_R\left(k\right)-R_Iw_I\left(k\right)-l_1w_R\left(k\right)\right],o_I\stackrel{\Delta }{=}\left\{o\left(k\right)\right\}=\frac{1}{l_2}\left[R_Iw_R\left(k\right)+R_Rw_I\left(k\right)-l_1w_I\left(k\right)\right],c\stackrel{\Delta }{=}q\left(o_R\right)+\mathrm{jq}\left(o_I\right),\sigma \stackrel{\Delta }{=}\sqrt{1-{w^H\left(k\right)\stackrel{\_}{o}\left(k\right)}^2},$

where:

l₁=w^H(k)Rw(k)

is the first Lagrange multiplier in Rw(k)=l₁ w(k)−l₂o. The operators and return the real and imaginary parts of the argument, respectively. With some algebra, it can shown that:

$\begin{array}{c}{D}_{\tilde{o}\left(k\right)}J\left(w\left(k\right)\right)=2\Re \left\{w^H\left(k\right)R\tilde{o}\left(k\right)\right\}\\ =\frac{2l_2}{c\sigma }\left[o_R^Tq\left(o_R\right)+o_I^Tq\left(o_I\right)\right]\\ >0.\end{array}$

wherein the strict inequality holds due to x^T q(x)≧0 and the fact that the increment vector o(k)≠0. If the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R, then from the above relationship it is clear that D_o(k)J(w(k))=0 for any increment vector õ(k) tangent to the equal-level surface g(w)=0 when the antenna weight vector w=w(k).

[0144] In support thereof, if D_õ(k)J(w(k))=0, then the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R. The aforementioned relationship can be proved by contradiction. Suppose the antenna weight vector w(k) is not an eigenvector of the channel correlation matrix R. From the following:

$\begin{array}{c}{D}_{\tilde{o}\left(k\right)}J\left(w\left(k\right)\right)=2\Re \left\{w^H\left(k\right)R\tilde{o}\left(k\right)\right\}\\ =\frac{2l_2}{c\sigma }\left[o_R^Tq\left(o_R\right)+o_I^Tq\left(o_I\right)\right],\end{array}$

the values of o_R−o_I=0, which leads to the increment vector o(k)=0. As a result of:

$o\left(k\right)=\frac{1}{l_2}\left[R-\left(w^H\left(k\right)\mathrm{Rw}\left(k\right)\right)I_M\right]w\left(k\right),$

then,

Rw(k)=l₁w(k),

which holds if the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R, which, of course, contradicts the hypothesis that the antenna weight vector w(k) is not an eigenvector of the channel correlation matrix R.

[0145] The above lemmas reveal the gradient-ascent behavior of the QFOA beamforming system. Therefore, the system converges to an eigenvector of the channel correlation matrix R (i.e., a local maximum of the received signal power J(w)). Furthermore, the QFOA beamforming system preferably converges to the global maximum due to the structure of the objective function of the received signal power J(w). It turns out that the received signal power J(w) has no local maximum over the constraint set S_g. This is verified by the following lemma.

[0146] Lemma 3: If λ₁>λ_i>λ_M, then the antenna weight vector w_i is a saddle point of the received signal power J(w) over the constraint set S_g. It is first noted that the antenna weight vector w_i is a stationary point of the received signal power J(w) over the constraint set S_g. This is due to the fact that the directional derivative of the received signal power J(w) in the direction of v_i is zero, where v_i is any unit vector parallel to the hyperplane tangent to the constraint set S_g at the antenna weight vector w=w_i. Indeed, since the antenna weight vector w_i=−∇_wH g(w_i) which is orthogonal to the equal-level surface g(w)=0, then v_i^H w_i=0. Hence, the directional derivative of the received signal power J(w) in the direction of v_i at the antenna weight vector w=w_i is:

D_viJ(w_i)=v_i^H∇_wHJ(w_i)+∇_wJ(w_i)v_i=v_i^Hλ_iw_i+w_i^Hλ_iv_i=0,

which establishes that the antenna weight vector w_i is a stationary point of the received signal power J(w) over the constraint set S_g. Next, it will be established that the antenna weight vector w_i is neither a local maximum point nor a local minimum point of the received signal power J(w) over the constraint set S_g. Since the received signal power J(w) is analytic in the antenna weight vector w and continuous over the constraint set S_g, it is sufficient to show that there exist vectors e and f with arbitrarily small magnitudes such that:

w_i+eεS_g,

w_i+fεS_g,

J(w_i+e)>J(w_i), and

J(w_i+f)<J(w_i).

Such vectors indeed exist. For example, for any complex scalars e₁, e₂, . . . , e_i−1, with e₁≠0, and for any complex scalars f_i+1, f_i+2, . . . , f_M with f_M≠0, if:

$e=\frac{e_1w_1+e_2w_2+\dots +e_{i-1}w_{i-1}+w_i}{\sqrt{{e_1}^2+{e_2}^2+\dots +{e_{i-1}}^2+1}}-w_i,\mathrm{and}$ $f=\frac{w_i+f_{i+1}w_{i+1}+f_{i+2}w_{i+2}+\dots +f_Mw_M}{\sqrt{1+{f_{i+1}}^2+{f_{i+2}}^2+{f_M}^2}}-w_i,$

then the following hold:

w_i+eεS_g,

w_i+fεS_g.

Note that ∥e∥→0 as e_k→0, k=1, 2, . . . , i−1, and ∥f∥→0 as f_l→0, l=i+1, i+2, . . . , M. Also, the relationships as set forth below:

$J\left(w_i+e\right)=\frac{{e_1}^2{\lambda }_1+{e_2}^2{\lambda }_2+\dots +{e_{i-1}}^2{\lambda }_{i-1}+{\lambda }_i}{{e_1}^2+{e_2}^2+\dots +{e_{i-1}}^2+1}>{\lambda }_1=J\left(w_i\right),J\left(w_i+f\right)=\frac{{\lambda }_1+{f_{i+1}}^2{\lambda }_{i+1}+{f_{i+2}}^2{\lambda }_{i+2}+\dots +{f_M}^2{\lambda }_M}{1+{f_{i+1}}^2+{f_{i+2}}^2+\dots +{f_M}^2}<{\lambda }_i=J\left(w_i\right)$

respectively satisfy:

J(w_i+e)>J(w_i), and

J(w_i+f)<J(w_i).

[0147] From the above lemmas, it can be seen that for the component-wise quantization defined by:

$\stackrel{\_}{o}\left(k\right)=\frac{q\left(o_R\left(k\right)\right)+\mathrm{jq}\left(o_I\left(k\right)\right)}{q\left(o_R\left(k\right)\right)+\mathrm{jq}\left(o_I\left(k\right)\right)},{\left\{q\left(x\right)\right\}}_I=\text{arg}\underset{v\in Q}{\min }x_I-v,\mathrm{and}$ $Q\stackrel{\Delta }{=}\left\{-q_{2^{b-1}-1},\dots ,-q_1,-q_0,q_0,q_1,\dots ,q_{2^{b-1}-1}\right\},$

that the directional derivative D_õ(k)J(w(k)) is nonnegative. Further, the sufficient condition for D_õ(k)J(w(k))≧0 does not necessarily require component-wise quantization as described by:

${\left\{q\left(x\right)\right\}}_I=\text{arg}\underset{v\in Q}{\min }x_I-v.$

[0148] Lemma 4 (quantization design criterion):

$\mathrm{Let}\stackrel{\_}{O}\left(k\right)\stackrel{\Delta }{=}{\left[{\stackrel{\_}{o}}_R^T\left(k\right),{\stackrel{\_}{o}}_I^T\left(k\right)\right]}^T$

be the quantized version of

$O\left(k\right)\stackrel{\Delta }{=}{\left[o_R^T\left(k\right)o_I^T\left(k\right)\right]}^T$

via a quantizer Q_k(·), i.e., Ō(k)=Q_k(O(k)). For any nonzero O(k) ε R^2M, let O_i(k) and {Q_k(O(k))}_i respectively denote the i-th components of O(k) and Q_k(O(k)), i=1, 2, . . . , 2M. Let S_k be any nonempty subset of {1, 2, . . . , 2M} such that O_i(k)≠0 for all i ε S_k. If {Q_k(O(k))}_iO_i(k)>0 for all i ε S_k and {Q_k(O(k))}_i=0 for all i ∉ S_k, then D_õ(k)J(w(k))≧0, with equality if and only if the antenna weight vector w(k) is an eigenvector of the channel correlation matrix R. The result follows from:

$\begin{array}{c}{D}_{\tilde{o}\left(k\right)}J\left(w\left(k\right)\right)=2\Re \left\{w^H\left(k\right)R\tilde{o}\left(k\right)\right\}\\ =\frac{2l_2}{c\sigma }\left[o_R^Tq\left(o_R\right)+o_I^Tq\left(o_I\right)\right].\end{array}$

[0149] This lemma implies that the number of feedback bits can be arbitrary and, hence, adaptable while maintaining the inequality D_õ(k)J(w(k))≧0. For instance, one or more nonzero components of O(k) can be quantized to their respective signs and the remaining components quantized to zero according to a predefined protocol which selects the index set S_k for the k-th slot. The set S_k can be periodic in k under practical considerations. In the extreme case where only one component of O(k) is quantized to its sign and the remaining components are quantized to zero, we have a one-bit feedback scheme.

[0150] Since the objective function the received signal power J(w) has no local maximum in the constraint set S_g, and the directional derivative is always nonnegative, the QFOA beamforming system converges to the global maximum. If the maximum eigenvalue of the channel correlation matrix R has multiplicity m, then w₁, w₂, . . . , w_m are the global maximum points of the received signal power J(w) in the constraint set S_g. However, how close the system can get to a global maximum point in the steady state depends on the step size. A smaller the step size μ in general yields a smaller steady-state error and a slower initial speed of convergence and vice versa.

[0151] Turning now to FIGS. 10 to 13, illustrated are graphical representations demonstrating exemplary instances of an evolution of a received signal power J(w(k)) for a randomly realized static channel including representations of a QFOA beamforming system. In the illustrated embodiments, the evolution of the received signal power J(w(k)) is obtained via a deterministic and random versions of the tangential perturbation gradient approximation system (labeled as “Random Tangential” and “Determ. Tangential”), the random perturbation method by B. C. Banister, et al. (labeled as “Random”), the deterministic perturbation method by B. Raghothaman (labeled as “Determ.”) and the QFOA beamforming system described herein. The illustrated embodiments employ the following parameters for the demonstrated instances with M transmit antennas.

	FIG.	M	b (bits)	μ (step size)
	10	4	1	0.2
	11	4	1	0.5
	12	4	2	0.2
	13	4	2	0.5

[0152] The curve designated optimal curve corresponds to the case where the exact optimal increment vector o(k) is fed back; this represents the ideal case where the number of quantization bits is b=∞. As expected, we see that the QFOA beamforming system has faster convergence speed than the other methods. Also, it is of practical interest to note that the QFOA beamforming system has a smaller steady-state error for a relatively large step size (μ=0.5) and experiences only a small loss in convergence speed compared to the ideal feedback case. It should be noted, however, that the perturbation methods employ a one-bit feedback while in these plots the QFOA beamforming system uses multiple-bit feedback.

[0153] Turning now to FIGS. 14A and 14B, illustrated are graphical representations comparing exemplary error rate performance of other systems with a QFOA beamforming system. In particular, an error rate performance of the QFOA beamforming system is provided illustrating the uncoded bit error rates of the QFOA beamforming system and other systems for a four transmit antenna, one receive antenna configuration with receiver velocities of 3 and 10 km/hr. The channel is generated according to a Jakes fading model. The BER curves correspond to the no beamforming transmission (M=1), a one bit feedback perturbation-based adaptive system, the quantized weighted feedback system where the sign of the real and imaginary parts of the weight vector are fed back, and a codebook selection method based on a Grassmannian codebook procedure. It should be noted that for the BER results, there is a delay in the application of the beam vector. That is, for practicality considerations, the weight vector updated in a given frame is used to transmit the next frame.

[0154] Thus, a QFOA beamforming system employing quantized feedback adaptive beamforming has been illustrated and described herein. The feedback information is a quantized version of an updated direction or incremental vector. It should be understood that with quantization schemes, the method has global convergence since the objective function has no local maximum in the constraint set and its directional derivative is nonnegative in the quantized increment vector. It is worth noting the flexibility of the QFOA beamforming system which allows the quantization resolution (number of feedback bits) to be arbitrary and, therefore, adaptable. Simulation results for a slow fading channel show that for the Grassmannian codebook-based method, the bit error rate tends to be somewhat inferior to those of the QFOA beamforming system and DPGA system at high SNRs, which confirms our earlier conjecture that the codebook-based method does not achieve a global maximizer. For low and medium SNRs, however, the QFOA beamforming system and the Grassmannian codebook method have similar performances. With scalar quantization schemes previously described, since the QFOA beamforming system does not search over a codebook, its complexity has virtually no dependency on the number of feedback bits.

[0155] Thus, in the environment of a communications system, a receiving station (e.g., mobile station) includes a receiver of a QFOA beamforming system that receives a forward-link signal including pilot signals from a transmitter of a base station employing multiple transmit antennas with different weighting components. The QFOA beamforming system also includes a detector embodied in the mobile station that measures characteristics of the forward-link signal in accordance with the pilot signals and provides a quantized increment vector that represents a preferable adaptation of the weightings for the weighting components to enhance a received signal quality. The QFOA beamforming system still further includes a transmitter embodied in the mobile station that transmits the quantized increment vector to the base station via a reverse-link signal. A beamformer selector (e.g., embodied in the base station) of the QFOA beamforming system operates to determine an updated, re-orthogonalized increment vector based on the quantized increment vector received from the mobile station. A weight vector modifier of the QFOA beamforming system produces new components of the weight vector selected by the beamformer selector, which are provided to a vector applicator for application to the corresponding weighting elements of the transmit antennas of the base station. The base station thereafter employs the updated weighting elements to transmit the forward-link signal to the mobile station.

[0156] Also, although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. For example, many of the processes discussed above can be implemented in different methodologies and replaced by other processes, or a combination thereof, to determine an updated beam direction based on the quantized increment vector received from the mobile station as described herein.

[0157] Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed, that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.