Dynamic Sparse Coded Multi-Hop Transmissions Using Reinforcement Learning

Ruifeng Gao, Ye Li, Jue Wang, Tony Q.S. Quek

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Packet transmissions over multi-hop lossy links are important in the future space-air-ground integrated networks. This letter considers sparse coded transmissions with reduced complexity compared to the well-known random linear network coding, which is known to be efficient in multi-hop lossy links. We propose a reinforcement learning framework for dynamically adjusting coding parameters on-line to improve the performance based on decoder feedback. A key advantage of the dynamic scheme is that it does not require a prior knowledge of the environment nor an analysis model. Extensive evaluation shows that it adapts well in scenarios where link conditions are unknown and/or changing, and achieves performance close to that of the optimal fixed schemes found by exhaustive search.

Original languageEnglish
Article number9126857
Pages (from-to)2206-2210
Number of pages5
JournalIEEE Communications Letters
Volume24
Issue number10
DOIs
Publication statusPublished - 2020 Oct

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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