Reinforcement Learning Based Efficient Underwater Image Communication

摘要

In this letter, we proposed an efficient underwater acoustic (UWA) image communication algorithm based on reinforcement learning which can improve the image quality while reduce the energy consumption and time delay in fast time variant UWA channels. In the proposed algorithm, the received image quality and other communication performance parameters are estimated at the sink continuously and then feedback to the sensor by an independent channel in order to avoid bandwidth loss caused by large time delay. At the sensor, the most suitable modulation and coding method is chosen to maximize a special designed value function in order to achieve the best efficient underwater image communication. Sea test results show that the proposed UWA image communication algorithm can reduce the bit-error rate by 3.1 dB, the energy consumption of the sensor by 26.9% and the time delay by 58.2%. The proposed algorithm can also shorten the convergence time by 47.4% compared with the model-free reinforcement learning underwater communication algorithm.