Adaptive CQI and RI Estimation for 5G NR: A Shallow Reinforcement Learning Approach

In this paper, we study the problem of estimating rank indicator (RI) and channel quality indicator (CQI) under 5G new radio (NR) specification. The main challenge for estimating RI and CQI comes from the fact that their estimation result is affected by many factors, e.g., delay spread, Doppler spread, etc. We use a shallow reinforcement learning technique to estimate the expected spectral efficiency for different RIs and CQIs. In particular, Q-learning is applied to solve the problem with a neural network consisting of a single hidden layer. To update the neural network output when the channel behavior changes, we develop an online adaptation (OA) algorithm working on top of the trained neural network. As a result, the combined algorithm adaptively controls the reported RI and CQI values to satisfy a target block error rate (BLER). We show through simulation results that the proposed algorithm adapts to different channel conditions and maintains the BLER to be around a target value of 10%. The proposed algorithm also significantly outperforms a scheme of fixed CQI and fixed RI when channel varies slowly.