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

Tae-Kyoung Kim1, Moonsik Min2,3

  • 1Department of Electronic Engineering, Gachon University, Seongnam 13120, Korea.

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|June 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a low-complexity reinforcement learning algorithm for channel estimation in multiple-input multiple-output (MIMO) systems. The method improves accuracy by intelligently using detected symbols, reducing estimation errors and enhancing performance.

Keywords:
Markov decision processchannel estimationmultiple-input multiple-outputreinforcement learning

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Accurate channel estimation is crucial for reliable data transmission in Multiple-Input Multiple-Output (MIMO) systems.
  • Conventional channel estimation methods often struggle with error accumulation due to noisy detected symbols.
  • Reducing complexity and latency in channel estimation algorithms is a key challenge for efficient wireless communication.

Purpose of the Study:

  • To propose a novel, low-complexity channel estimation algorithm for MIMO systems.
  • To enhance channel estimation accuracy by selectively utilizing detected data symbols as pilots.
  • To optimize symbol selection using a reinforcement learning approach for improved performance.

Main Methods:

  • Formulating the symbol selection problem as a Markov Decision Process (MDP).
  • Developing an efficient reinforcement learning algorithm to solve the MDP and derive an optimal policy.
  • Implementing a closed-form solution using backup samples and data subblocks to minimize latency and computational complexity.

Main Results:

  • The proposed algorithm significantly reduces the Minimum Mean Square Error (MMSE) of channel estimates.
  • Block error rate (BLER) performance is substantially improved compared to conventional channel estimation techniques.
  • The algorithm achieves reduced latency and lower computational complexity, making it practical for real-time applications.

Conclusions:

  • The reinforcement learning-based channel estimator offers a significant advancement in MIMO system performance.
  • Selective utilization of detected symbols, guided by an optimal policy, effectively mitigates estimation errors.
  • The proposed low-complexity approach provides a viable solution for enhancing wireless communication reliability.