Method And Apparatus For Performing Finite Memory Network Coding In An Arbitrary Network

[0001] The present application claims the benefit of U.S. Provisional Patent Application No. 61/599,224 filed on Feb. 15, 2012, which is incorporated by reference herein in its entirety.

[0002] This work was supported by the United States Space and Naval Warfare Systems Command (SPAWAR) under Contract No. N66001-11-C-4003. The Government has certain rights in this invention.

[0003] The subject matter disclosed herein relates generally to information transfer and, more particularly, to distribution of data among a plurality of nodes using network coding.

[0004] Network coding is a technique that may be used in a wireless or wired network to improve the information flow of the network. In a conventional network, a node acting as a relay node will typically forward packets (or messages) in the network by re-transmitting the packets in the form that they were received. In a network that uses network coding, on the other hand, relay nodes may combine a number of received packets together into a coded packet before forwarding the coded packet in the network. A node in the network that receives the coded packet may then store the coded packet for eventual decoding. Random linear network coding (RLNC) is a form of network coding that uses randomly generated coefficients to form linear combinations of packets to be forwarded in a network. RLNC has been shown to be a powerful technique for achieving robust, high throughput multi-cast packet distribution in certain network environments. However, RLNC can require some nodes in a network to maintain a considerable amount of data in a local memory to support coding operations. In many instances a node may be called upon to store every packet it has received. In addition, as the amount of packet data stored in a node increases, the computational complexity of the coding operation may increase by a proportional amount. There is a general need for techniques that are capable of reducing the memory requirements and/or computational complexity of implementing RLNC and/or other forms of network coding in arbitrary networks.

[0005] Techniques are disclosed herein that are capable of reducing the memory requirements and/or computational complexity required to perform network coding in an arbitrary network or system. In some implementations, a network node may provide only a limited amount of memory space for performing network coding. For example, a network node may only allow a finite number of packets to be stored in the node for use in performing network coding operations (e.g., S packets, where S is a positive integer). In at least one implementation, a network node may include both a coding buffer and a decoding buffer. The coding buffer may be provided to support network coding operations for the node when used as a relay node and the decoding buffer to support decoding operations for the node when used as a destination node. In some implementations, the decoding buffer may store every packet received by the node for use in packet decoding, while the coding buffer only stores a limited, finite number of packets for coding purposes. If a node is not interested in decoding data, it can include only a coding buffer and not a decoding buffer (or deactivate a decoding buffer if present).

[0006] Because only a finite number of packets are stored for network coding purposes, the complexity of network coding operations may be significantly reduced. For example, in a network that practices random linear network coding (RLNC), the coding function generally requires all packets stored for coding purposes to be retrieved from memory and processed to generate a packet to be transmitted by the node. By limiting the number of packets that are stored for coding purposes, fewer packets may have to be retrieved and processed to generate a coded packet for transmission. In addition, because fewer packets are retrieved, less random coefficients need to be generated during the network coding process, resulting in a further reduction in computational complexity. Because less memory is required for coding purposes, a lower capacity, faster form of memory may be used for the coding buffer in some implementations.

[0007] In accordance with one aspect of the concepts, systems, circuits, and techniques described herein, a machine implemented method for operating a network node in a memory efficient manner in a network having a plurality of nodes that does not use pro-established routing to direct packets through the network, comprises: (a) receiving a new packet at the network node from an arbitrary direction; (b) modifying contents of a coding buffer of the network node using the new packet, the coding buffer for use in performing network coding for the network node, the coding buffer to store no more than S packets for use in network coding, where S is a positive integer, wherein modifying the contents of the coding buffer includes linearly combining the new packet with packets already stored in the coding buffer to generate modified packets and storing the modified packets in the coding buffer, (c) generating a new packet to be transmitted from the network node after modifying contents of the coding buffer, wherein generating a new packet includes linearly combining packets stored in the coding buffer using network coding; and (d) transmitting the new packet to one or more possibly unknown other nodes.

[0008] In accordance with another aspect of the concepts, systems, circuits, and techniques described herein, a node device for use in an network having a plurality of nodes that does not use pro-established routing to direct packets through the network, comprises: (a) a receiver to receive data packets from a surrounding environment, wherein packets can be received from any arbitrary direction in the network; (b) a coding buffer to store packets received from the surrounding environment for use in network coding, the coding buffer to store no more than S packets for use in network coding, where S is a positive integer; (c) a buffer content modifier to modify contents of the coding buffer when a new data packet is received from the surrounding environment; (d) a network encoder to generate, using network coding, a coded packet for transmission from the node device using packet data stored in the coding buffer; and (e) a transmitter to transmit the coded packet to one or more possibly unknown other nodes.

[0009] In accordance with a still another aspect of the concepts, systems, circuits, and techniques described herein, an apparatus is provided that includes a computer readable storage medium having instructions stored thereon that, when executed by one or more processors of a computing system, operate to perform a method for operating a node in a network having a plurality of nodes that does not use pre-established routing to direct nodes through the network. More specifically, the method comprises: (a) obtaining a new packet at the node, the new packet having been received at the node from an arbitrary direction; (b) modifying contents of a coding buffer of the node using the new packet, the coding buffer for use in performing network coding at the node, the coding buffer to store no more than S packets for use in network coding, where S is a positive integer, wherein modifying the contents of the coding buffer includes linearly combining the new packet with packets stored in the coding buffer to generate modified packets and storing the modified packets in the coding buffer; (c) generating a new packet to be transmitted from the node, wherein generating a new packet includes linearly combining data stored in the coding buffer using network coding; and (d) causing the new packet to be transmitted from the node to one or more possibly unknown other nodes.

[0010] In accordance with a further aspect of the concepts, systems, circuits, and techniques described herein, a node device is provided for use in a network having a plurality of nodes where, at any one time, the plurality of nodes can include multiple source nodes, multiple destination nodes, and multiple relay nodes to relay packets between source nodes and destination nodes. more specifically, the node device comprises: (a) a coding buffer to store received packets for use in performing network coding to generate new packets if the node device is being used as a relay node, the coding buffer having a small, fixed number of memory locations that can be used for storing packets for use in network coding operations; (b) a coding buffer content modifier to modify contents of the coding buffer when a new data packet is received from a surrounding environment; and (c) a decoding buffer to store received packets for use in decoding operations if the node device is being used as a destination device, the decoding buffer being different from the coding buffer, wherein the decoding buffer is to store each new packet received by the node device for use in decoding operations with no fixed limit on the number of memory locations that can be used for decoding; wherein the node device is capable of concurrent operation as two or more of: a source node, a relay node, and a destination node.

[0017] FIG. 1 is a schematic diagram illustrating an example network 10 that may incorporate technical features described in the present disclosure. As shown, network 10 may include a number of nodes 12, 14, 16, 18, 20, 22, 24, 26 that are able to communicate with one another via links 28 between the nodes. In a typical communication scenario, one or more of the nodes 12, 14, 16, 18, 20, 22, 24, 26 may have information that needs to be distributed to some or all of the other nodes. In FIG. 1, for example, node 12, node 14, and node 16 may each have information that needs to be distributed to node 22, node 24, and node 26. As used herein, the term “source node” will refer to a node that has information to be distributed to other nodes and the term “destination node” will refer to a node that is intended to receive information. Thus, in the example network of FIG. 1, node 12, node 14, and node 16 may be source nodes and node 22, node 24, and node 26 may be destination nodes. It should be appreciated that different source nodes may have different amounts of information to be distributed to destination nodes. For example, with reference to FIG. 1, node 12 may have 4 messages to be distributed, node 14 may have 2 messages to be distributed, and node 16 may have 3 messages to be distributed. In the illustrated example, destination nodes 22, 24, 26 are each to receive all transmitted messages. To distribute messages in a network, one or more relay nodes may be used to relay messages between nodes. For example, with reference to FIG. 1, node 18 and node 20 may be used as relay nodes to relay messages from source nodes 12, 14, and 16 to destination nodes 22, 24, and 26. As used herein, the word “message” and the word “packet” may be used interchangeably.

[0018] In the network 10 of FIG. 1, all of the source nodes 12, 14, 16 are located on the left, all of the destination nodes 22, 24, 26 are located on the right, and all of the relay nodes 18, 20 are located in the middle. It should be appreciated that this scenario was used to simplify illustration and description and may not be representative of a typical network arrangement. That is, in an actual network, source nodes, destination nodes, and relay nodes may be located anywhere within the network. For example, in one scenario, node 18 in FIG. 1 may be a source node with a single message to transmit and may also wish to receive all messages in the network. Similarly, node 14 may have no messages to send, but may wish to receive all messages. Also, node 24 may have two messages to transmit, but may not want to receive any messages.

[0019] Although nodes 12, 14, 16, 18, 20, 22, 24, 26 of FIG. 1 have each been described above as being either a source node, a relay node, or a destination node, it should be understood that a single node may have multiple different purposes in a network. For example, node 18 may serve as both a relay node and a destination node in some network scenarios. Likewise, node 16 may serve as both a source node and a destination node in some situations. In a broadcast scenario, all (or most) nodes in a network may be destination nodes. In some embodiments, some or all of the nodes in a network may configured to act as both relay nodes and destination nodes (or relay nodes, destination nodes, and source nodes). It should be understood that the network 10 of FIG. 1 is merely an example of one possible network arrangement that may exist. In general, networks may have any number of different nodes and any number of different network topologies. In addition, as will be described in greater detail, the techniques and structures described herein are not limited to use within communication networks. That is, in some implementations, the techniques may be used in other types of systems that are not typically considered communication networks (e.g., data storage systems that store data in a distributed manner, etc.).

[0020] In some networks, the topology of the network may change over time. In addition, the reliability and/or capacity of the links of the network may change over time, with some links becoming unreliable or unavailable and other links becoming available that were not previously available. One typical example of such dynamics are wireless mesh networks in which availability, capacity, and reliability of node-to-node connections can vary greatly due to, for example, movement of the nodes, obstructions coming between nodes, increased contention between nodes, and other changing conditions. In such scenarios, traditional routing techniques may not be the best method for distributing messages through a network. Network coding has been suggested as an intelligent option for distributing messages in a network in certain circumstances. In network coding, combinations of different messages may be made at certain nodes of a network to generate coded packets to be forwarded in the network. Destination nodes in the network may then store received coded packets in a memory and use these stored coded packets, along with any knowledge they have of the original transmitted messages, to decode the coded packets and recover all of the originally transmitted messages.

[0021] In one type of network coding, known as random linear network coding (RLNC), relay nodes in a network may generate a coded packet to be forwarded in the network by linearly combining received messages using randomly generated coefficients. In some instances, a transmitted packet may also include an indication of the coefficients used in the linear combination. Thus, the transmitted packets may, in some implementations, have the format ({right arrow over (μ)}, {right arrow over (m)}), where {right arrow over (m)}=Σ_l=1^kμ_l{right arrow over (m_l)} εF_q^s is a linear combination of messages {right arrow over (m₁)}, . . . , {right arrow over (m_k)} and {right arrow over (μ)}=(μ₁, . . . , μ_k)εF_q^k is the vector of coefficients used to generate the linear combination. The messages {right arrow over (m₁)}, . . . , {right arrow over (m_k)} are in this case seen as s-dimensional vector over a finite field F_q. For example, in the case q=2 they would be bit-vectors of length s. It should be appreciated that when s is much larger than k the size s+k of a packet is dominated by the size of a message making the overhead of sending the coefficients along negligible. The coefficients {right arrow over (μ)} may be used by a destination node during, for example, a decoding process. It has been shown that network coding is capable of enhancing overall throughput and providing better performance than traditional routing techniques in certain network scenarios. One example system and method for implementing RLNC is described in U.S. Pat. No. 7,706,365 to Effros et al. entitled “Randomized Distributed Network Coding,” which is incorporated by reference herein in its entirety.

[0022] In some past networks implementing RLNC, nodes operating as relay nodes to generate and forward coded packets typically stored every packet they received for use in coding. As can be appreciated, this practice may require a large amount of storage space to be reserved for coding purposes within a node. In addition, this technique may involve a relatively large computational complexity to perform network coding. In general, a network coding operation may involve reading the entire contents of a received packet memory and processing the retrieved information to generate a new coded packet. When there is a large volume of data in the memory, this processing may be very complex. This level of processing complexity may not be possible in some lower cost, lower complexity nodes having limited processing power. In addition, this high computational complexity can result in a higher power consumption within a node device that can significantly reduce battery life. In various implementations described herein, techniques and structures are provided that may reduce the memory requirements for implementing RLNC and other forms of network coding. The described techniques and structures are also capable of significantly reducing the computational complexity of implementing RLNC and other forms of network coding in a node device. In addition, the techniques and structures described herein may also provide a significant reduction in power consumption within a node device implementing RLNC in some implementations.

[0023] In various implementations, the techniques and structures described herein (i.e., finite memory network coding) may be implemented within networks that utilize a non-routing based approach to packet/message distribution. That is, the techniques may be used in networks where network nodes have little to no knowledge of when they are going to have a chance to forward packets or where the packets will be forwarded to. This is in contrast to traditional routing approaches where a next hop is selected before a packet is even transmitted. In such networks or systems, the disclosed techniques may allow nodes to keep a very small amount of information on hand for forwarding, while causing little to no degradation in packet distribution performance. It has been shown that, in most network settings, finite memory RLNC matches the performance of conventional RLNC. In addition, it has been shown that, for large enough q, the finite memory RLNC protocol is the best method for utilizing a given buffer size (i.e., no other protocol can finish faster or transmit more information) (see, for example, “One Packet Suffices—Highly Efficient Packetized Network Coding With Finite Memory,” by Haeupler et al., 2011 IEEE International Symposium on Information Theory (ISIT) Proceedings, pages 1151-1155, Jul. 31 2011-Aug. 5 2011 and “Optimality of Network Coding in Packet Networks,” by Hacupler et al., 2011 IEEE Information Theory Workshop (ITW) Proceedings, pages 533-537, October 2011, both of which are incorporated by reference herein in their entireties).

[0024] FIG. 2 is a block diagram illustrating an example node device architecture 40 that may incorporate features described in the present disclosure in one or more implementations. As illustrated, the node device architecture 40 may include: one or more digital processors 42, a fast memory 44, a transceiver 46, a slow memory 48, and a user interface 50. A bus 52 and/or other structure(s) may be provided for establishing interconnections between various components of device architecture 40. Digital processor(s) 42 may include one or more digital processing devices that are capable of executing programs to provide functions and/or services to a user. Fast memory 44 and slow memory 48 are digital data storage structures that may be used to store data and/or programs for other elements of node device architecture 40. User interface 50 may include any type of device, component, or subsystem that provides an interface between a user and a corresponding node device. As will be appreciated, in some types of nodes, a user interface may not be present. Transceiver 46 may include any type of transceiver that is capable of supporting communication with one or more remote entities.

[0025] Digital processor(s) 42 may include, for example, one or more general purpose microprocessors, digital signals processors (DSPs), controllers, microcontrollers, special purpose processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), programmable logic devices (PLDs), reduced instruction set computers (RISCs), and/or others, including combinations of the above. Digital processor(s) 42 may be used to, for example, execute an operating system of a corresponding node device. Digital processor(s) 42 may also be used to, for example, execute one or more application programs for the node device. In addition, digital processor(s) 42 may be used to implement, either partially or fully, one or more of the communications related processes or techniques described herein in some implementations.

[0026] As described above, transceiver 46 may include any type of transceiver that is capable of supporting communication with one or more remote entities. In various implementations, transceiver 46 may be configured in accordance with one or more networking standards. In some implementations, multiple transceivers may be provided to support operation in different networks or systems in a surrounding. Transceiver 46 may be capable of communicating with peer or infrastructure devices in a wireless, peer-to-peer, ad-hoc, or wired network arrangement. In some implementations, transceiver 46 may be used to implement, either partially or fully, one or more of the communications related processes or techniques described herein. It should be appreciated that the techniques described in the present disclosure may, in some embodiments, be implemented in other types of networks (e.g., memory systems, etc.).

[0027] Fast memory 44 may include one or more faster forms of digital data storage within a node device and slow memory 48 may include one or more slower forms of digital data storage in a node device. Typically, faster memory structures (e.g., flash memory, semiconductor random access memory (RAM), etc.) may have lower storage capacities and slower memory structures (e.g., a hard disk drive, etc.) may have higher storage capacities. Fast memory 44 may include, for example, one or more semiconductor memories, random access memories (RAMs), flash memories, USB drives, cache memories, erasable programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), and/or others. Similarly, slow memory 48 may include, for example, disc based storage devices, magnetic data storage devices, optical storage devices, compact disc read only memories (CD-ROMs), DVDs, Blu-Ray disks, magneto-optical disks, magnetic or optical cards, and/or others. As will be described in greater detail, the techniques described in the present disclosure may, in some implementations, allow fast memory to be used for network coding applications that may have formally been limited to slower forms of memory.

[0028] It should be appreciated that the node device architecture 40 of FIG. 2 represents one possible example of an architecture that may be used in an implementation. Other architectures may alternatively be used. As used herein, the term “node device” or “node” is used to describe any type of digital electronic device that includes some form of communication capability. This may include, for example, a laptop, desktop, notebook, or tablet computer; a personal digital assistant (PDA); a personal communication service (PCS) device; a personal navigation assistant (PNA); a cellular telephone, smart phone, or other communication device; a pager; a sensor device; a satellite communication device; a server, a router, a switch; a media player having communication capability; a digital storage device used in a distributed data storage system; and/or other devices. It should be appreciated that all or part of the various devices, processes, or methods described herein may be implemented using any combination of hardware, firmware, and/or software.

[0029] As described above, various techniques and structures are provided herein that may be used to reduce the memory requirements for implementing RLNC and/or other forms of network coding. These techniques and structures may also, or alternatively, be capable of significantly reducing the computational complexity and/or power consumption associated with the use of RLNC and/or other forms of network coding. In various implementations, these techniques may involve a limitation in a number of packets that may be stored in a node device for use in the performance of coding operations when forwarding packets in a network or system. For example, instead of storing every received packet for use in coding, a node device may be limited to the storage of a finite number of packets (e.g., S packets, where S is a positive integer). When a new packet is received by a node device, instead of storing the new packet directly in a local memory for use in network coding operations, the device may linearly combine the new packet with S packets already stored in the memory. This technique may be referred to herein as finite memory random linear network coding (FM-RLNC). Different techniques for performing the linear combination may be used. Two variants of the RLNC technique that will be described herein are accumulator FM-RLNC and recombinator FM-RLNC. When a packet is to be transmitted from an FM-RLNC enabled node, only S packets may need to be read from a memory to generate the coded packet to be transmitted. For this reason, the memory requirements and computational complexity of the coding operation may be greatly reduced over prior network coding strategies.

[0030] FIG. 3 is a block diagram illustrating an example processing arrangement 60 within a node device that may be used to perform accumulator FM-RLMC in an implementation. As illustrated, the processing arrangement 60 may include: a transceiver 62, an accumulator 64, a network encoder 66, a coding buffer 68, a decoding buffer 70, and a decoder 72. In the illustrated embodiment, transceiver 62 is a transceiver that is capable of receiving packets from and transmitting packets to a surrounding environment. In other implementations, another form of communication device may be used to send and receive packets. Coding buffer 68 is used to store information for use in network coding operations to generate packets to be forwarded from transceiver 62 to other nodes. Decoding buffer 70 is used to store received packets for eventual decoding within the node. In nodes of the past that implemented network coding, a single memory resource was typically used to store packets for use in both network coding operations and decoding operations. As will become apparent, the use of different memory resources to perform the two functions may permit a significant reduction in processing complexity during network coding operations.

[0031] In at least one implementation, coding buffer 68 is limited to storing a finite number of active packets (e.g., S packets) for use in coding operations. Accumulator 64 is operative for updating the S packets stored in coding buffer 68 when a new packet is received by transceiver 62. If there are less than S packets stored in coding buffer 68 when transceiver 62 receives a new packet, accumulator 64 may simply store the new packet directly in coding buffer 68. However, if S packets are already stored, accumulator 64 will update the S stored packets by linearly combining the new packet with the stored packets. For example, if S=5, and the currently stored packets are identified as P₁, P₂, P₃, P₄, and P₅, then accumulator 64 may update the stored packets as follows in one implementation:

P′₁=P₁+α₁P_new

P′₂=P₂+α₂P_new

P′₃=P₃+α₃P_new

P′₄=P₄+α₄P_new

P′₅=P₅+α₅P_new

where P′₁, P′₂, P′₃, P′₄, and P′₅ are the updated packets; P_new is the new packet; and α₁, α₂, α₃, α₄, and α₅ are randomly generated coefficients. A random number generator may be used to generate the random coefficients.

[0032] To perform the updates, accumulator 64 may retrieve all S stored packets from coding buffer 68, modify the packets, and then store the modified packets in coding buffer 68. To further reduce complexity and memory requirements, however, accumulator 64 may in some implementations modify the stored packets one at a time. For example, accumulator 64 may first retrieve stored packet P₁ and then modify the packet by adding α₁P_new to generate a coded packet P′₁. Accumulator 64 may then store the coded packet P′₁ in coding buffer 68 before retrieving the next packet P₂ for modification, and so on. In this manner, accumulator 64 may only require storage for a single packet to perform the buffer updates (i.e., to store the packet being modified). The complexity of this approach is θ(S). Thus, a relatively simple processor may be used to perform the updates. In addition, because coding buffer 68 is limited to storing a finite number of active packets, a faster, lower capacity form of memory may be used. For example, if the node device architecture 40 of FIG. 2 is used, fast memory 44 may be used for coding buffer 68 rather than the higher capacity slow memory 48. In network coding enabled devices of the past that stored all received packets for use in network coding, a higher capacity slower form of memory was invariably used to store received packets for coding purposes.

[0033] Network encoder 66 is operative for performing the network encoding required when a packet needs to be transmitted by transceiver 62. Network encoder 66 may, for example, retrieve the S packets from coding buffer 68 and linearly combine the packets using randomly generated coefficients to generate a new packet. In some implementations, network encoder 66 may then append the set of coefficients to the coded packet before delivering it to transceiver 62 for transmission. Because only a fixed, finite number of packets are retrieved and combined, computational complexity may be significantly less then prior techniques. For example, as described previously, past systems stored every received packet for use in coding operations. When these packets where combined during a coding operation, a random number generator may have been called upon to generate a random number for each of the stored packets. Using FM-RLNC, the number of random numbers that need to be generated during a coding operation are greatly reduced, further reducing computational complexity.

[0034] As described above, when transceiver 62 receives a new packet, it may send the packet to accumulator 64 for use in updating a finite number of packets in coding buffer 68. In addition, the new packet may be delivered to decoding buffer 70 to be stored for later decoding. Unlike coding buffer 68, decoding buffer 70 may store all packets received by transceiver 62 for use in decoding. As such, in some implementations, decoding buffer 70 may utilize a slower, higher capacity form of memory within a node device (e.g., slow memory 48 of FIG. 2), while coding buffer 68 may utilize a faster, lower capacity form of memory (e.g., fast memory 44 of FIG. 2). When sufficient data is stored in decoding buffer 70, decoder 72 may decode the data to recover messages originally transmitted in the network. Techniques for decoding data using network encoded packets (included RLNC encoded packets) are well known in the art. If a node is not interested in decoding messages, then updates to decoding buffer 70 may be suspended. If a node is never interested in decoding messages (e.g., in some situations, a node may only serve as a relay node), decoding buffer 70 and decoder 72 may not be needed. In these cases, a simpler and less expensive node device may be used.

[0035] In some implementations, the node device of FIG. 3 is adapted for use in an arbitrary network where input may be received from any arbitrary direction. For example, with reference to FIG. 3, packets may be received from remote nodes over any of a variety of different links 74, 76, 78. In addition, the location of the remote nodes may change with time. In some embodiments, pre-established routing is not used. The node device of FIG. 3 may simply act as a relay for a received packet, receiving it from an arbitrary source node and then broadcasting a coded packet in response thereto. Therefore, in some embodiments, the node device may not know from where it will receive a next packet or to where a next coded packet will be transmitted. In some implementations, the node device of FIG. 3 may be adapted to act as both a relay node and a destination node. In such an implementation, the node device of FIG. 3 may store received packets in decoding buffer 70 for eventual decoding. In some embodiments, a node device may be capable of simultaneously serving as two or more of a source node, a destination node, and a relay node.

[0036] FIG. 4 is a block diagram illustrating an example processing arrangement 80 within a node device that may be used to perform recombinator FM-RLMC in an implementation. As illustrated, the processing arrangement 80 may include: a transceiver 82, a recombinator 84, a network encoder 86, a coding buffer 88, a decoding buffer 90, and a decoder 92. Thus, the processing arrangement 80 of FIG. 4 is similar to the arrangement 60 of FIG. 3, but with accumulator 64 replaced by recombinator 84. Coding buffer 88 may again be limited to a finite number of active packets (i.e., S packets). Recombinator 84 is operative for updating the S packets stored in coding buffer 88 when a new packet is received by transceiver 82. As with accumulator 64 discussed previously, if there are less than S packets stored in coding buffer 88 when transceiver 82 receives a new packet, recombinator 84 may simply store the new packet directly in coding buffer 68. However, if S packets are already stored, recombinator 84 may update the S stored packets using random linear combinations. For example, if S=5, and the currently stored packets are identified as P₁, P₂, P₃, P₄, and P₅, then recombinator 84 may update the stored packets as follows in one implementation:

P′₁=α₁₁P₁+α₁₂P₂+α₁₃P₃+α₁₄P₄+α₁₅P₅+β₁P_new

P′₂=α₂₁P₁+α₂₂P₂+α₂₃P₃+α₂₄P₄+α₂₅P₅+β₁P_new

P′₃=α₃₁P₁+α₃₂P₂+α₃₃P₃+α₃₄P₄+α₃₅P₅+β₁P_new

P′₄=α₄₁P₁+α₄₂P₂+α₄₃P₃+α₄₄P₄+α₄₅P₅+β₁P_new

P′₅=α₅₁P₁+α₅₂P₂+α₅₃P₃+α₅₄P₄+α₅₅P₅+β₁P_new

where P′₁, P′₂, P′₃, P′₄, and P′₅ are the updated packets; P_new is the new packet; and α₁₁, . . . , α₁₅; α₂₁, . . . , α₂₅; α₃₁, . . . , α₃₅; α₄₁, . . . , α₄₅; α₅₁, . . . , α₅₅; and β₁, . . . , β₅ are randomly generated coefficients. Other linear combination techniques may be used in other implementations. A random number generator may be used to generate the random coefficients.

[0037] To perform the updates, recombinator 84 may retrieve all S stored packets from coding buffer 88, modify the packets, and then store the modified packets in coding buffer 88. As will be appreciated, this technique may be more computationally complex than the accumulator FM-RLMC variant described previously in connection with FIG. 3. However, this approach is still much less complex than traditional RLMC. For example, this technique may be shown to have a complexity of θ(S²), while traditional RLMC has a complexity of θ(k) where k is the number of transmitted messages. As described previously, if a node is not interested in decoding messages, then decoding buffer 90 and decoder 92 may be optional.

[0038] In some implementations, the number of packets S stored in a coding buffer of a node may be varied over time. In general, the selection of a value for S may involve a tradeoff between performance and computational complexity. For example, higher values of S may result in improved performance and lower values of S may result in lower complexity. In at least one implementation, S may adapt with time to changing network conditions. For example, the value of S may adapt based on traffic conditions in the network. In one possible approach, two different values of S (S₁ and S₂) may be used. The first value S₁ may be used when more consistent traffic exists in the network and the second value S₂ may be used if traffic becomes bursty. Additional values may also be specified. Other techniques for varying the value of S used by a node during network operation may alternatively be used.

[0039] In some implementations, the node device of FIG. 4 is adapted for use in an arbitrary network where input may be received from any arbitrary direction. For example, with reference to FIG. 4, packets may be received from remote nodes over any of a variety of different links 94, 96, 98. In addition, the location of the remote nodes may change with time. In some embodiments, pre-established routing is not used. The node device of FIG. 4 may simply act as a relay for a received packet, receiving it from an arbitrary source node and then broadcasting a coded packet in response thereto. In some implementations, the node device of FIG. 4 may be adapted to act as both a relay node and a destination node. In such an implementation, the node device of FIG. 4 may store received packets in decoding buffer 90 for eventual decoding.

[0040] FIG. 5 is a flowchart illustrating a method 100 for use in supporting FM-RLNC within a node device in an implementation. A new packet is first received at a node (block 102). If data decoding is desired, the new packet may be stored within a decoding buffer of the node (block 104). If there are already S packets stored within the coding buffer, the new packet may then be linearly combined with the S packets in the buffer (block 106). Any of a variety of different approaches may be used to perform the linear combination. For example, as discussed previously, an accumulator FM-RLNC approach may be used where, for each of S stored packets, a linear combination of the new packet and the stored packet is made to generate a combined packet to be stored in the corresponding memory location. In another possible implementation, a recombinator FM-RLNC approach may be used where, for each of the S stored packets, a random linear combination of all S stored packets and the new packet is made. Other techniques for modifying the S packets stored in the coding buffer may alternatively be made. When a transmission is to be made by the node device, the packet data within the coding buffer may be retrieved and linearly combined to form a coded packet for transmission. In at least one approach, a randomly generated coefficient may be generated for each of the retrieved packets to be used in the combination. A list of the coefficients used to perform the coding may, in some implementations, be included with the coded packet for possible use during a future decoding operation at a destination node.

[0041] In the description above, various implementations and variations have been discussed in the context of multicast or broadcast transmission within a communication network. It should be appreciated, however, that the described techniques and structures also have application in a unicast transmission scenario. In addition, the techniques and structures also have application in systems not typically considered communications networks, such as digital data storage systems that store information in a distributed manner. As described previously, the techniques discussed herein have application in both wireless and wired systems.

[0042] The techniques and structures described herein may be implemented in any of a variety of different forms. For example, features of the invention may be embodied within various forms of communication devices, both wired and wireless; television sets; set top boxes; audio/video devices; laptop, palmtop, desktop, and tablet computers with or without wireless capability; personal digital assistants (PDAs); telephones; pagers; satellite communicators; cameras having communication capability; network interface cards (NICs) and other network interface structures; base stations; access points; integrated circuits; as instructions and/or data structures stored on machine readable media; and/or in other formats. Examples of different types of machine readable media that may be used include floppy diskettes, hard disks, optical disks, compact disc read only memories (CD-ROMs), digital video disks (DVDs), Blu-ray disks, magneto-optical disks, read only memories (ROMs), random access memories (RAMs), erasable programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), magnetic or optical cards, flash memory, and/or other types of media suitable for storing electronic instructions or data.

[0043] In the foregoing detailed description, various features of the invention are grouped together in one or more individual embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects may lie in less than all features of each disclosed embodiment.

[0044] Having described implementations which serve to illustrate various concepts, structures, and techniques which are the subject of this disclosure, it will now become apparent to those of ordinary skill in the art that other implementations incorporating these concepts, structures, and techniques may be used. Accordingly, it is submitted that that scope of the patent should not be limited to the described implementations but rather should be limited only by the spirit and scope of the following claims.