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Hybrid Deployment Optimization Algorithm for Reconfigurable Intelligent Surface.

Yifan Lin1, Xinwei Lin2, Zhiyu Han1

  • 1The Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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|December 11, 2025
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Summary
This summary is machine-generated.

This study introduces a hybrid algorithm for efficient reconfigurable intelligent surface (RIS) deployment, improving wireless communication quality in shadowed areas by optimizing signal gain.

Keywords:
coverage blind spotsdeployment optimizationhybrid algorithmreconfigurable intelligent surfacetwo-stage branch-and-bound method

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Area of Science:

  • Wireless communication
  • Intelligent surfaces
  • Optimization algorithms

Background:

  • Reconfigurable intelligent surfaces (RIS) are key 6G technologies.
  • RIS integration in sensor-communication systems supports positioning and sensing.
  • Efficient RIS deployment is crucial for mitigating wireless communication blind spots.

Purpose of the Study:

  • Propose a hybrid optimization algorithm for efficient RIS deployment.
  • Address NP-hard combinatorial optimization problems in RIS allocation.
  • Improve signal gain and communication quality in shadow areas.

Main Methods:

  • Decompose the optimization problem into two stages: greedy strategy and Branch-and-Bound (BnB).
  • Greedy strategy allocates locally optimal RIS to shadow areas for coverage completeness.
  • BnB algorithm optimizes global RIS deployment to maximize signal gain.

Main Results:

  • The hybrid algorithm reduces computational complexity for large-scale problems.
  • Greedy phase ensures fair coverage.
  • BnB-based optimization achieves up to 56.85% higher average SINR gain in shadow areas compared to random deployment.

Conclusions:

  • The proposed hybrid algorithm effectively optimizes RIS deployment.
  • Improved SINR gain enhances communication quality for users in shadow areas.
  • Overall network performance is significantly improved by the optimized RIS deployment.