Q-Learning based Link Adaptation in 5G

Abstract

This paper proposes a novel method of constructing a Q-learning framework for link adaptation (LA) in the fifth generation (5G) mobile network. The state-action function is approximated via a neural network (NN). The state space relies on the hybrid automatic repeat request (HARQ) and channel state information (CSI) reports from the user equipment (UE). The output of the Q-learning based LA (QLA) approach consists of the assigned modulation and coding schemes (MCSs) and number of layers, which are used to construct the action space. Reward is calculated based on the HARQ information and the transmit block size (TBS). System level simulation in a typical indoor hotspot scenario has been performed, showing that the proposed QLA outperforms the ordinary LA approach in terms of user throughput.