INCdeep: Intelligent Network Coding with Deep Reinforcement Learning

In this paper, we address the problem of building adaptive network coding coefficients under dynamic network conditions (e.g., varying link quality and changing number of relays). In existing linear network coding solutions including deterministic network coding and random linear network coding, coding coefficients are set by a heuristic or randomly chosen from a Galois field with equal probability, which can not adapt to dynamic network conditions with good decoding performance. We propose INCdeep, an adaptive Intelligent Network Coding with Deep Reinforcement Learning. Specifically, we formulate a coding coefficients selection problem where network variations can be automatically and continuously expressed as the state transitions of a Markov decision process (MDP). The key advantage is that IN-Cdeep is able to learn and dynamically adjust the coding coefficients for the source node and each relay node according to ongoing network conditions, instead of randomly. The results show that INCdeep has generalization ability that adapts well in dynamic scenarios where link quality is changing fast, and it converges fast in the training process. Compared with the benchmark coding algorithms, INCdeep shows superior performance, including higher decoding probability and lower coding overhead through simulations and experiments. The average CPU usage and end-to-end delay when deploying INCdeep in practical are almost the same with benchmark algorithms.