发表机构
IIT Hyderabad（印度海得拉尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何通过链路自适应算法选择MCS类型，提出联合汤普森采样算法，利用MCS成功概率有序的特点，采用多元有序贝塔分布作为先验，解决现有算法在特定场景的不足，实现各场景下有竞争力的吞吐量和稳健性能。
AI 中文摘要
特定信道条件下调制与编码（MCS）类型的选择通过MAC层的链路自适应（LA）算法进行。这些算法依赖ACK/NACK统计和信道质量指标（CQI）反馈。现有工作将LA建模为跨蜂窝和Wi-Fi链路的多臂老虎机（MAB）问题。本文利用MCS成功概率有序这一事实，提出联合汤普森采样（Joint-TS）算法。与经典TS不同，Joint-TS使用多元有序贝塔分布作为先验，以保留成功概率的固有单调性。仿真结果表明，现有MAB算法在特定场景中失败，而Joint-TS在所有场景中都具有有竞争力的吞吐量和稳健、一致的性能。
英文摘要
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.
CommentsSPCOM 2026
