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Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems.

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Summary

This review explores how Reinforcement Learning (RL) and Deep Learning (DL) can address challenges in Multiple-Input Multiple-Output (MIMO) systems, enhancing 5G wireless communication. These AI techniques offer solutions for data rate, reliability, and complexity issues in MIMO technology.

Keywords:
BSCSIMIMO systemschannel estimationdeep learningdetection communicationlocalizationmmWave communicationpositioningreinforcement learningresource allocationsignal

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Area of Science:

  • Wireless Communication Systems
  • Artificial Intelligence in Telecommunications
  • Signal Processing

Background:

  • Exponential growth in mobile traffic necessitates high data rate, reliability, and low latency in wireless infrastructure.
  • Multiple-Input Multiple-Output (MIMO) systems, including Multi-User MIMO and Massive MIMO, are key 5G technologies for high throughput.
  • Exploiting multiple antennas and managing computational complexity remain significant challenges in MIMO systems.

Purpose of the Study:

  • To provide a comprehensive review of Reinforcement Learning (RL) and Deep Learning (DL) techniques applied to MIMO systems.
  • To explore how RL and DL can mitigate inherent challenges in MIMO communication.
  • To present potential applications of RL and DL across various MIMO functionalities.

Main Methods:

  • Review of existing literature integrating RL and DL with MIMO systems.
  • Explanation of fundamental concepts in RL, DL, and MIMO.
  • Categorization of RL and DL applications based on specific MIMO issues.

Main Results:

  • RL and DL offer powerful tools to overcome MIMO system limitations.
  • Identified applications span MIMO detection, channel estimation, localization, CSI feedback, security, and resource allocation.
  • Demonstrated potential for AI to enhance performance in mmWave communications.

Conclusions:

  • The integration of RL and DL presents a promising avenue for advancing MIMO communication technologies.
  • AI-driven solutions are crucial for meeting the demands of future wireless networks.
  • Further research into RL and DL applications can unlock significant improvements in MIMO system efficiency and capability.