Bayesian Link Adaptation under a BLER Target

The optimal modulation and coding scheme (MCS) for wireless transmission depends on the dynamic wireless channel state. Hence, wireless link adaptation relies on periodically reported channel quality index (CQI) values to select the optimal MCS for each transmission instance. However, to optimize link performance for a given wireless environment, current link adaptation techniques rely on tuning parameters such as a block error rate (BLER) target and algorithm adjustments that are difficult to optimize heuristically. Here, we propose BayesLA, a Bayesian link adaptation scheme that does not require careful parameter tuning for optimal link performance in diverse wireless environments. BayesLA, which is inspired by the Thompson Sampling approach widely used for online learning, efficiently learns the MCS success probabilities conditioned on the reported CQI values. Through numerical simulations for a Rayleigh fading wireless channel and a typical cellular link configuration, we demonstrate that BayesLA outperforms state-of-the-art outer loop link adaptation (OLLA) approach in terms of the realized link throughput for a given BLER target.