Dynamic Sparse Coded Multi-Hop Transmissions Using Reinforcement Learning

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.