用深度强化学习优化双跳中继通信，实现低时延高可靠传输。
Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning
- 分两跳独立调节参数：子载波间隔、微时隙大小、调制编码方案
- 在严格时延约束下接近最优可靠性，满足URLLC要求
- 适合5G/6G工业控制、自动驾驶等对时延敏感场景
下一代无线通信系统需支持超可靠低时延通信（URLLC）以服务关键任务应用。实现严格的URLLC要求极具挑战性，尤其在双跳协作通信中。本文针对双跳中继通信系统设计自适应传输方案，每跳独立调整传输参数，包括物理层参数配置（numerology）、mini-slot大小及调制编码方案（MCS），以在严苛时延约束下实现可靠包传输。将各跳的收发机配置建模为马尔可夫决策过程（MDP），提出基于双智能体强化学习的协同低时延传输算法（DRL-CoLA），实现分布式学习低时延感知传输策略。仿真结果表明，所提算法在满足严格时延要求的同时，达到近似最优可靠性。
原文摘要 · Abstract (English)
Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications. Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication. In this paper, we develop an adaptive transmission design for a two-hop relaying communication system. Each hop transmission adaptively configures its transmission parameters separately, including numerology, mini-slot size, and modulation and coding scheme, for reliable packet transmission within a strict latency constraint. We formulate the hop-specific transceiver configuration as a Markov decision process (MDP) and propose a dual-agent reinforcement learning-based cooperative latency-aware transmission (DRL-CoLA) algorithm to learn latency-aware transmission policies in a distributed manner. Simulation results verify that the proposed algorithm achieves the near-optimal reliability while satisfying strict latency requirements.
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