This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation.
Keywords: Markov decision process; channel estimation; multiple-input multiple-output; reinforcement learning.