Adaptive Causal Network Coding with Feedback for Multipath Multi-hop Communications
Alejandro Cohen, Guillaume Thiran, Vered Bar Bracha, Muriel, M\'edard

TL;DR
This paper introduces an adaptive, causal network coding algorithm for multipath multi-hop communications that optimizes throughput and delay using feedback, packet allocation, and decentralized balancing, outperforming traditional ARQ protocols.
Contribution
It presents a novel adaptive, causal coding scheme with decentralized optimization and selective recoding for multipath multi-hop networks, improving throughput and delay performance.
Findings
Achieves over 90% of channel capacity with zero error in non-asymptotic regime.
Demonstrates up to 2x throughput gains over ARQ.
Achieves more than 3x delay reduction compared to baseline.
Abstract
We propose a novel multipath multi-hop adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction. This algorithm generalizes our joint optimization coding solution for point-to-point communication with delayed feedback. AC-RLNC is adaptive to the estimated channel condition, and is causal, as the coding adjusts the retransmission rates using a priori and posteriori algorithms. In the multipath network, to achieve the desired throughput and delay, we propose to incorporate an adaptive packet allocation algorithm for retransmission, across the available resources of the paths. This approach is based on a discrete water filling algorithm, i.e., bit-filling, but, with two desired objectives, maximize throughput and minimize the delay. In the multipath multi-hop setting, we propose a new decentralized balancing optimization algorithm. This balancing…
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