Low-latency Sliding-window Recoding

Tasdemir, Elif; Tömösközi, Máté; Salah, Hani; Fitzek, Frank H. P.

doi:10.1109/vtc2021-spring51267.2021.9449001

Cited by 2 publications

(3 citation statements)

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“…While recoding in intermediate network nodes has been extensively studied for BATS codes, see e.g., [53]- [55], to the best of our knowledge there has been no prior detailed study of recoding for sparse systematic RLNC with preservation of the sparsity property. This journal article extends the related preliminary conference papers [1], [2] by introducing the novel Rec-woDec strategy and the novel rateless recoding approach. Also, this journal article provides substantially extended evaluations compared to [1], [2] by including the recoding throughput as well as evaluations for different numbers of intermediate nodes.…”

Section: B Related Workmentioning

confidence: 67%

“…This journal article extends the related preliminary conference papers [1], [2] by introducing the novel Rec-woDec strategy and the novel rateless recoding approach. Also, this journal article provides substantially extended evaluations compared to [1], [2] by including the recoding throughput as well as evaluations for different numbers of intermediate nodes.…”

Section: B Related Workmentioning

confidence: 67%

“…Then, the destination counts the number G−µ of received uncoded and decoded original source packets to evaluate the packet loss according to Eqn. (2). Subsequently, the source proceeds to the next generation.…”

Section: B Evaluation Metrics 1) Packet Lossmentioning

confidence: 99%

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SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

et al. 2021

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Sparse Random Linear Network Coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, 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 coding coefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the low density of non-zero coding coefficients 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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