Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

Abstract

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

A Novel Joint Channel Estimation and Symbol Detection Receiver for Orthogonal Time Frequency Space in Vehicular Networks

“…In some intricate scenarios such as underwater acoustic communication with a t-distribution noise, model-based approaches may fall short in addressing channel estimation. Due to the robust data-driven capabilities of deep learning, it is frequently employed to tackle previously intractable issues [ 14 , 15 , 16 ]. For instance, the authors in [ 15 ] employed a deep residual convolution neural network to deal with noise in RIS channels.…”

“…For instance, the authors in [ 15 ] employed a deep residual convolution neural network to deal with noise in RIS channels. In [ 16 ], the authors leveraged reinforcement learning to address MIMO channel estimation, addressing the computational complexity of algorithms and the time-varying nature of the channel. Inspired by the successful application of deep learning, we employed an innovative neural-network-based technique for OTFS channel estimation.…”