用联合贝塔分布优化无线链路调制编码选择，提升稳定性和吞吐量。
Link Adaptation Using Joint-Thompson Sampling
- 基于有序贝塔分布设计联合汤普森采样算法，保留调制编码阶数的单调性
- 在多种信道条件下均实现稳定高吞吐，优于传统多臂赌博机方法
- 适合需要鲁棒链路自适应的5G/Wi-Fi系统部署
在无线通信中，链路自适应（LA）通过物理层反馈的确认/否定信号和信道质量指示（CQI）决定调制编码方案（MCS）。现有工作将该问题建模为多臂赌博机（MAB），每个MCS视为一个伯努利臂，其成功概率为参数。经典算法如上置信界（UCB）和汤普森采样（TS）被广泛应用。本文提出联合汤普森采样（Joint-TS），利用多元有序贝塔分布作为先验，以保持不同MCS间成功概率的单调性。仿真结果表明，传统MAB算法在特定场景下表现不佳，而联合TS在所有场景中均表现出色，具备竞争性吞吐量与鲁棒一致性。
原文摘要 · Abstract (English)
The choice of Modulation and Coding (MCS) type for a particular channel condition is made through link adaptation (LA) algorithms that operate at the MAC layer. These algorithms rely on the ACK/NACK statistics and the channel quality index (CQI) feedback. Several existing works model LA as a multi-armed bandit (MAB) problem across cellular and Wi-Fi links. In the MAB formulation, each available MCS is a Bernoulli arm parameterized by its transmission success probability, and the goal is to design a selection strategy that accrues maximum reward. Several popular MAB algorithms, such as upper confidence bound (UCB) and Thompson Sampling (TS), have been proposed in the literature. Using the fact that MCS success probabilities are ordered, we propose the Joint-Thompson Sampling (Joint-TS) algorithm. Unlike classical TS, which assumes independent Beta distributions for each arm, Joint-TS utilizes a multivariate ordered Beta distribution as the prior to preserve the inherent monotonicity of success probabilities. Our simulation results show that while existing MAB algorithms fail in specific scenarios, Joint-TS delivers competitive throughput with robust, consistent performance in all scenarios.
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