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Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement

Min Wu1, Shibing Zhu1, Changqing Li1

  • 1School of Space Information, Space Engineering University, Beijing 101416, China.

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Summary

This study introduces a deep reinforcement learning (DRL) approach for optimizing reconfigurable intelligent surface (RIS)-assisted integrated satellite networks. The DRL method enhances system performance and enables real-time decision-making for improved network efficiency.

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deep reinforcement learning (DRL)integrated satellite–HAP–terrestrial networks (IS-HAP-TNs)optimization performancereconfigurable intelligent surface (RIS)

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Area of Science:

  • Wireless Communication Networks
  • Intelligent Reflecting Surfaces
  • Satellite Communications

Background:

  • Integrated satellite and terrestrial networks face challenges in optimizing performance.
  • Reconfigurable intelligent surfaces (RIS) offer potential for signal enhancement.
  • High-altitude platforms (HAP) provide stable network infrastructure.

Purpose of the Study:

  • To enhance the performance of integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) using RIS.
  • To jointly optimize the transmit beamforming and RIS phase shift matrices for maximum system sum rate.
  • To address the complexity of combinatorial optimization problems in RIS-assisted networks.

Main Methods:

  • Development of a deep reinforcement learning (DRL) algorithm for online decision-making.
  • Joint optimization of ground user equipment (UE) transmit beamforming and RIS phase shift.
  • Simulation experiments to evaluate the proposed DRL algorithm's effectiveness.

Main Results:

  • The proposed DRL algorithm significantly outperforms standard schemes.
  • Demonstrated improvements in system performance, execution time, and computing speed.
  • Feasibility of real-time decision-making for joint optimization problems was confirmed.

Conclusions:

  • DRL is an effective approach for optimizing RIS-assisted IS-HAP-TNs.
  • The proposed method enables efficient and real-time network management.
  • RIS-assisted networks with DRL offer a promising direction for future wireless communication systems.