Deep Reinforcement Learning Empowered Rate Selection of XP-HARQ

TL;DR

This paper proposes a deep reinforcement learning approach to optimize rate selection in XP-HARQ systems over correlated fading channels, aiming to maximize long-term throughput despite outdated channel information.

Contribution

It introduces a novel DRL-based method using DDPG for rate selection in XP-HARQ, transforming the problem into an MDP and demonstrating its effectiveness.

Findings

DRL-based rate selection outperforms traditional methods.

The proposed scheme achieves higher throughput.

Simulation confirms the scheme's superiority over benchmarks.

Abstract

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(LTAT) is maximized by properly choosing the incremental information rate for each HARQ round on the basis of the outdated channel state information (CSI) available at the transmitter.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.