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Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication.

Xin Liu1,2, Shanghong Zhao1, Yanxia Liang3

  • 1School of Information and Navigation, Air Force Engineering University, Xi'an, China.

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|February 3, 2025
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Summary
This summary is machine-generated.

This study introduces a novel equalization algorithm for uncrewed aerial vehicle (UAV) cluster communications. The U-FRQL-EA algorithm enhances dynamic sensing and channel equalization, improving communication quality and resource utilization.

Keywords:
Adaptive perceptionBlind equalizationDynamic equalizationFuzzy reinforcement Q learningU-Net

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Area of Science:

  • * Electrical Engineering
  • * Computer Science
  • * Artificial Intelligence

Background:

  • * Uncrewed aerial vehicle (UAV) cluster communications face challenges in dynamic sensing and channel equalization.
  • * Existing methods struggle with real-time adaptation to complex channel environments and noise reduction.

Purpose of the Study:

  • * To develop an advanced equalization algorithm for UAV communication systems.
  • * To enhance dynamic sensing, channel equalization, and resource utilization in UAV networks.

Main Methods:

  • * Development of a U-Net-based signal processing algorithm for noise reduction and real-time channel state perception.
  • * Enhancement of fuzzy reinforcement Q-learning with a fuzzy neural network for improved Q-value approximation and adaptability.
  • * Integration of the U-Net model and enhanced fuzzy reinforcement Q-learning into the U-FRQL-EA algorithm.

Main Results:

  • * The U-FRQL-EA algorithm significantly reduces the bit error rate (BER) in UAV communication systems.
  • * Demonstrated enhancement in overall communication quality and network resource optimization.
  • * Effective real-time channel state sensing and intelligent data forwarding strategies.

Conclusions:

  • * The U-FRQL-EA algorithm offers a novel and effective solution for improving UAV communication performance.
  • * The integration of U-Net and fuzzy reinforcement Q-learning addresses key challenges in dynamic sensing and equalization.
  • * The proposed method optimizes network resource utilization and enhances communication reliability.