Abstract: Described herein are systems, methods, and other techniques for compatible packet separation for communication networks. A block comprising a plurality of packets to be transmitted over a network is received. The block includes a set of batches, and the plurality of packets are distributed between the set of batches. A pseudo interleaver depth is calculated for each of the set of batches to produce a set of pseudo interleaver depths. Blockwise adaptive recoding is performed using the set of pseudo interleaver depths to produce a number of recoded packets for each of the set of batches. A transmission sequence is generated using the number of recoded packets for each of the set of batches.

Abstract: Described herein are systems, methods, and other techniques for compatible packet separation for communication networks. A block comprising a plurality of packets to be transmitted over a network is received. The block includes a set of batches, and the plurality of packets are distributed between the set of batches. A pseudo interleaver depth is calculated for each of the set of batches to produce a set of pseudo interleaver depths. Blockwise adaptive recoding is performed using the set of pseudo interleaver depths to produce a number of recoded packets for each of the set of batches. A transmission sequence is generated using the number of recoded packets for each of the set of batches.

Abstract: Hardware acceleration for batched sparse (BATS) codes is enabled. Hardware implementation of some timing-critical procedures can effectively offload computationally intensive overheads, for example, finite field arithmetic, Gaussian elimination, and belief propagation (BP) calculations, and this can be done without direct mapping of software codes to a hardware implementation. Suitable acceleration hardware may include pipelined multipliers configured to multiply input data with coefficients of a matrix associated with a random linear network code in a pipelined manner, addition components configured to add multiplier output to feedback data, and switches to direct data flows to and from memory components such that valid result data is not overwritten and such that feedback data corresponds to most recent valid result data. Acceleration hardware components (e.g., number and configuration) may be dynamically adjusted to modify BATS code parameters and adapt to changing network conditions.

Abstract: Hardware acceleration for batched sparse (BATS) codes is enabled. Hardware implementation of some timing-critical procedures can effectively offload computationally intensive overheads, for example, finite field arithmetic, Gaussian elimination, and belief propagation (BP) calculations, and this can be done without direct mapping of software codes to a hardware implementation. Suitable acceleration hardware may include pipelined multipliers configured to multiply input data with coefficients of a matrix associated with a random linear network code in a pipelined manner, addition components configured to add multiplier output to feedback data, and switches to direct data flows to and from memory components such that valid result data is not overwritten and such that feedback data corresponds to most recent valid result data. Acceleration hardware components (e.g., number and configuration) may be dynamically adjusted to modify BATS code parameters and adapt to changing network conditions.

Abstract: A method for data encoding and associated decoding is based on the concept of batches that allows transmission of a large data file from a source node to multiple destination nodes through communication networks that may employ network coding wherein sparse matrix codes are employed in a network setting. A batch is a set of packets generated by a subset of the input packets using sparse matrix encoder. A sparse matrix encoder can be called repeatedly to generate multiple batches. The batches are generally independent of one another. During the transmission in a communication network, network coding can be applied to packets belonging to the same batch to improve the multicast throughput. A decoder recovers all or at least a fixed fraction of the input packets using received batches. The input packets can be pre-coded using a pre-code before applying sparse matrix codes. The data file can then be reconstructed by further decoding the pre-code.

Abstract: A method for data encoding and associated decoding is based on the concept of batches that allows transmission of a large data file from a source node to multiple destination nodes through communication networks that may employ network coding wherein sparse matrix codes are employed in a network setting. A batch is a set of packets generated by a subset of the input packets using sparse matrix encoder. A sparse matrix encoder can be called repeatedly to generate multiple batches. The batches are generally independent of one another. During the transmission in a communication network, network coding can be applied to packets belonging to the same batch to improve the multicast throughput. A decoder recovers all or at least a fixed fraction of the input packets using received batches. The input packets can be pre-coded using a pre-code before applying sparse matrix codes. The data file can then be reconstructed by further decoding the pre-code.