Link Adaptation Using Joint-Thompson Sampling

2026-07-13

AI Summary

This work addresses the challenge of link adaptation in wireless communications, where the goal is to dynamically select the optimal modulation and coding scheme (MCS) to maximize throughput. Conventional multi-armed bandit (MAB) approaches often exhibit unstable performance because they neglect the inherent monotonicity in MCS success probabilities—higher-order MCSs typically yield lower success rates under identical channel conditions. To overcome this limitation, the paper formulates link adaptation as an MAB problem and introduces a novel Thompson sampling algorithm that employs a joint ordered Beta distribution as its prior, explicitly preserving the monotonic structure among MCS success rates. By integrating both ACK/NACK and channel quality indicator (CQI) feedback, the proposed method achieves consistently high and competitive throughput across diverse channel scenarios, significantly outperforming existing MAB-based approaches.

Abstract

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.

Problem

• Link Adaptation
• Modulation and Coding Scheme
• Multi-Armed Bandit
• Thompson Sampling
• Channel Quality Index

Innovation

• Joint-Thompson Sampling
• Link Adaptation
• Modulation and Coding Scheme (MCS)
• Ordered Beta Distribution
• Multi-armed Bandit