Throughput Maximization for Polar Coded IR-HARQ Using Deep Reinforcement Learning

Abstract

The wireless channel conditions in the future mobile communication systems will become more and more complex as we are developing higher frequency bands, thus it is necessary to adjust the transmission parameters frequently. To ensure the reliability of data transmission, hybrid automatic repeat request (HARQ) techniques are widely used to improve the data throughput of wireless communication systems. This paper develops a polar coded incremental redundancy HARQ (IR-HARQ) scheme based on deep reinforcement learning (DRL) to combat the unexpected channel fluctuations in practice. Specifically, the IR bits are generated by performing quasi-uniform puncturing and polarizing matrix extension on polar codes, and the number of IR bits are optimized by utilizing the deep deterministic policy gradient (DDPG) algorithm in the considered IR-HARQ scheme. Simulation results show that compared with the conventional chase combing scheme and the fixed-length IR-HARQ scheme, the proposed IR scheme can significantly improve the system throughput.