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

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 th...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 22; číslo 12; s. 4379
Hlavní autoři: Kim, Tae-Kyoung, Min, Moonsik
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 09.06.2022
MDPI
Témata:
Accuracy
Algorithms
channel estimation
Markov decision process
multiple-input multiple-output
Optimization
Probability
Random variables
reinforcement learning
Sensors
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ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22124379