Transformer Offline Reinforcement Learning for Downlink Link Adaptation

Abstract: Recent advancements in Transformers have unlocked a new relational analysis technique for Reinforcement Learning (RL). This thesis researches the models for DownLink Link Adaptation (DLLA). Radio resource management methods such as DLLA form a critical facet for radio-access networks, where intricate optimization problems are continuously resolved under strict latency constraints in the order of milliseconds. Although previous work has showcased improved downlink throughput in an online RL approach, time dependence of DLLA obstructs its wider adoption. Consequently, this thesis ventures into uncharted territory by extending the DLLA framework with sequence modelling to fit the Transformer architecture. The objective of this thesis is to assess the efficacy of an autoregressive sequence modelling based offline RL Transformer model for DLLA using a Decision Transformer. Experimentally, the thesis demonstrates that the attention mechanism models environment dynamics effectively. However, the Decision Transformer framework lacks in performance compared to the baseline, calling for a different Transformer model.