Two-Stage Thompson Sampling for Outer-Loop Link Adaptation

In this letter, we consider outer-loop link adaptation, wherein we pursue to find the optimum modulation and coding scheme (MCS) that provides the maximum throughput without directly estimating the channel state. We cast this problem in a form of multi-armed bandit, an online decision making policy based on sequential observations. To efficiently solve a formulated problem, we propose a novel two-stage Thompson sampling. The proposed method is built based on the observation that the optimum MCS level appears in a certain group that satisfies feasibility conditions. Exploiting this feature, we find the optimum MCS level via two stages. In the first stage, we identify a group that has high probability of including the optimum MCS. In the second stage, we only focus on the MCS levels within the identified group in the first stage, and investigate the optimum MCS. By doing this, the search space is significantly reduced, which leads to the performance improvement. Simulation results show that the proposed method outperforms the existing state-of-art algorithm.