SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

Elif Tasdemir, Mate Tomoskozi, Juan A. Cabrera, Frank Gabriel, Dongho You, Frank H.P. Fitzek, Martin Reisslein

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)168567-168586
Number of pages20
JournalIEEE Access
StatePublished - 2021


  • Intermediate network node
  • low coding coefficient density
  • multi-hop network
  • random linear network coding (RLNC)
  • recoding
  • sliding window coding
  • sparse coding
  • systematic packet

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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