Deep Reinforcement Learning Empowered Rate Selection of XP-HARQ

• Published in: IEEE communications letters Vol. 27; no. 9; p. 1
• Main Authors: Wu, Da, Feng, Jiahui, Shi, Zheng, Lei, Hongjiang, Yang, Guanghua, Ma, Shaodan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automatic repeat request; Cross-packet hybrid automatic repeat request (XP-HARQ); Deep learning; deep reinforcement learning (DRL); Fading channels; Markov processes; Measurement; outdated channel state information; Power system reliability; Probability; rate selection; Redundancy; Reinforcement learning; Systems design; Throughput; Transmitters
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2023.3298931

The complex transmission mechanism of cross-packet hybrid automatic repeat request (XP-HARQ) hinders its optimal system design. To overcome this difficulty, this letter attempts to use the deep reinforcement learning (DRL) to solve the rate selection problem of XP-HARQ over correlated fading channels. In particular, the long term average throughput (LTAT) is maximized by properly choosing the incremental information rate for each HARQ round on the basis of the outdated channel state information (CSI) available at the transmitter. The rate selection problem is first converted into a Markov decision process (MDP), which is then solved by capitalizing on the algorithm of deep deterministic policy gradient (DDPG) with prioritized experience replay. The simulation results finally corroborate the superiority of the proposed XP-HARQ scheme over the conventional HARQ with incremental redundancy (HARQ-IR) and the XP-HARQ with only statistical CSI.