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Dynamic Sparse Coded Multi-Hop Transmissions Using Reinforcement Learning
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DOI：10.1109/LCOMM.2020.3005349.png)
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Abstract
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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.
Keywords:
Decoding
Encoding
Relays
Propagation losses
Spread spectrum communication
Network coding
Propagation delay
Sparse network coding
lossy relay links
reinforcement learning
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