用强化学习优化量化反馈，提升超可靠低时延通信的吞吐量
Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC
- 基于强化学习动态调整量化反馈策略
- 提出新方法估计瑞利-基因子以适配时变信道
- 适合时变信道环境下追求高可靠低时延的系统设计
本文针对超可靠低时延通信（URLLC）中的量化反馈问题，构建了完整的系统模型，研究在动态信道条件和量化反馈机制下的吞吐量最大化。接收端向发送端提供量化信道状态信息，系统通过强化学习自适应调整反馈方案，以提升整体吞吐量并适应不断变化的信道统计特性。文中提出一种新型的瑞利-K因子估计技术，使系统能够优化反馈策略。该动态方法显著提升性能，适用于实际中信道统计随时间变化的URLLC应用场景。
原文摘要 · Abstract (English)
This paper presents a comprehensive system model for goodput maximization with quantized feedback in Ultra-Reliable Low-Latency Communication (URLLC), focusing on dynamic channel conditions and feedback schemes. The study investigates a communication system, where the receiver provides quantized channel state information to the transmitter. The system adapts its feedback scheme based on reinforcement learning, aiming to maximize goodput while accommodating varying channel statistics. We introduce a novel Rician-$K$ factor estimation technique to enable the communication system to optimize the feedback scheme. This dynamic approach increases the overall performance, making it well-suited for practical URLLC applications where channel statistics vary over time.
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