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Adaptive Channel Estimation for Semi-Passive IRS with Optimized Sensor Deployment.

Zhiyu Han1, Hanning Wang2, Yafeng Wang1

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

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|November 13, 2025
PubMed
Summary
This summary is machine-generated.

Accurate channel state information (CSI) is crucial for Intelligent Reflective Surfaces (IRS). This study introduces a novel compressed sensing algorithm and sensor deployment for semi-passive IRS, enhancing CSI acquisition accuracy and reducing estimation overhead.

Keywords:
Intelligent Reflecting Surface (IRS)channel estimationcompressed sensingparticle swarm optimization (PSO)

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Metamaterials

Background:

  • Intelligent Reflective Surfaces (IRS) offer significant potential for passive beamforming.
  • Accurate Channel State Information (CSI) acquisition is essential for optimal IRS performance.
  • Passive IRS devices present challenges in obtaining precise CSI due to their signal processing limitations.

Purpose of the Study:

  • To develop an improved channel estimation algorithm for semi-passive IRS.
  • To enhance the accuracy of CSI acquisition in IRS systems.
  • To minimize the overhead associated with channel estimation.

Main Methods:

  • A compressed sensing channel estimation algorithm is proposed for semi-passive IRS, not requiring prior knowledge of the channel path number.
  • Particle Swarm Optimization (PSO) is utilized to determine the optimal deployment scheme for active sensors within the semi-passive IRS.
  • Numerical simulations are conducted to validate the proposed methods.

Main Results:

  • The proposed compressed sensing algorithm improves channel estimation accuracy.
  • The PSO-based sensor deployment scheme effectively optimizes active sensor placement.
  • A reduction in Normalized Mean Square Error (NMSE) is observed, indicating enhanced estimation precision.
  • Fewer pilot symbols are required for effective channel estimation, reducing overhead.

Conclusions:

  • The developed methods significantly enhance CSI acquisition for semi-passive IRS.
  • The proposed approach offers a practical solution for improving IRS performance in wireless communication systems.
  • This work contributes to reducing the computational and pilot overhead in IRS-based systems.