Exploiting sparse coding: A sliding window enhancement of a random linear network coding scheme
Pablo Garrido, David Gómez, Jorge Lanza, Ramón Agüero · 2016
Random Linear Network Coding (RLNC) is a technique that provides several benefits. For instance, when applied over wireless mesh networks, it can be exploited to ease routing solutions as well as to increase the robustness against packet losses. Nevertheless, the complexity of the decoding process and the required overhead might jeopardize its performance. There is a trade-off when deciding the field and block sizes; larger values decrease the probability of transmitting linearly dependent packets, but they also increase both the required overhead and the decoding complexity. In order to overcome these limitations, we propose a sliding window enhancement; a fixed number of packets (fewer than the block size) is combined within every transmission, and the decoding process can therefore take advantage of the algebra with sparse matrices. The paper presents an analytical model, which is first validated and later broaden by means of an extensive simulation campaign carried out over the ns-3 simulator.