A Low-Complexity Algorithm for a Reinforcement Learning-Based Channel Estimator for MIMO Systems.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, c2000-

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.

Sensors (Basel). 2021 Jul 16;21(14):. (PMID: 34300599)
Sensors (Basel). 2021 Dec 31;22(1):. (PMID: 35009848)

2021R1F1A1063273 National Research Foundation of Korea; 2020R1F1A1071649 National Research Foundation of Korea; 4199990113966 Ministry of Education

Keywords: Markov decision process; channel estimation; multiple-input multiple-output; reinforcement learning

Date Created: 20220624 Date Completed: 20220627 Latest Revision: 20220716

20240628

PMC9229451

10.3390/s22124379

35746162