Sparse Random Linear Network coding (RLNC) reduces the computational complexity of the RLNC decoding through a lowdensity of the non-zero codingcoefficients, which can be achieved through sending uncoded (systematic...
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Sparse Random Linear Network coding (RLNC) reduces the computational complexity of the RLNC decoding through a lowdensity of the non-zero codingcoefficients, which can be achieved through sending uncoded (systematic) packets. However, conventional recoding of sparse RLNC coded packets at an intermediate node in a multi-hop network increases the density of non-zero codingcoefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the lowdensity of non-zero codingcoefficients of sparse RLNC with systematic packets. We develop SpaRec strategies with and without decoding at the intermediate nodes, with and without a specified coding rate, as well as with finite and infinite recoding window lengths. We evaluate the SpaRec strategies in multi-hop networks in terms of packet loss, packet delivery delay, as well as recoding and decoding (computation) throughput. We find that the SpaRec strategies substantially improve the RLNC performance compared to conventional recoding.
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