Reinforcement Learning for Delay Sensitive Uplink Outer-Loop Link Adaptation

Link adaptation (LA) – selecting the modulation and coding scheme (MCS) – is the process where the transmission format is adjusted to prevailing channel conditions, to achieve a balance between spectral efficiency and reliability. New 5G use cases with increased reliability and low latency demands, together with limited user equipment (UE) transmission power budgets, make this task ever more challenging, particularly in adverse propagation environments and interference scenarios.Usually machine learning -based algorithms for link adaptation are aiming at maximizing spectral efficiency. In this paper we study extended reality (XR) uplink traffic and show how online reinforcement learning (RL) can be applied in LA to meet XR’s stringent demands for uplink traffic. In particular, we propose a practical Q-learning as well as deep Q network - based outer-loop link adaptation (OLLA) algorithms that aim at using minimal amount of radio resources for delivering packets within the packet delay budget (PDB). By striving for low radio resource usage multi-agent RL related greediness problems can be mitigated. Realistic system level simulations confirm that the proposed algorithm outperforms traditional OLLA when high reliability and low delays are required. It is also shown that the challenging requirements, set by XR uplink traffic, can be met.