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A Low-Complexity Solution for Optimizing Binary Intelligent Reflecting Surfaces towards Wireless Communication

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Listed:
  • Santosh A. Janawade

    (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Mangalore 575025, India)

  • Prabu Krishnan

    (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Mangalore 575025, India)

  • Krishnamoorthy Kandasamy

    (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Mangalore 575025, India)

  • Shashank S. Holla

    (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Mangalore 575025, India)

  • Karthik Rao

    (Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Mangalore 575025, India)

  • Aditya Chandrasekar

    (Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Mangalore 575025, India)

Abstract

Intelligent Reflecting Surfaces (IRSs) enable us to have a reconfigurable reflecting surface that can efficiently deflect the transmitted signal toward the receiver. The initial step in the IRS usually involves estimating the channel between a fixed transmitter and a stationary receiver. After estimating the channel, the problem of finding the most optimal IRS configuration is non-convex, and involves a huge search in the solution space. In this work, we propose a novel and customized technique which efficiently estimates the channel and configures the IRS with fixed transmit power, restricting the IRS coefficients to { 1 , − 1 } . The results from our approach are numerically compared with existing optimization techniques.The key features of the linear system model under consideration include a Reconfigurable Intelligent Surface (RIS) setup consisting of 4096 RIS elements arranged in a 64 × 64 element array; the distance from RIS to the access point measures 107 m. NLOS users are located around 40 m away from the RIS element and 100 m from the access point. The estimated variance of noise N C is 3.1614 × 10 − 20 . The proposed algorithm provides an overall data rate of 126.89 (MBits/s) for Line of Sight and 66.093 (MBits/s) for Non Line of Sight (NLOS) wireless communication.

Suggested Citation

  • Santosh A. Janawade & Prabu Krishnan & Krishnamoorthy Kandasamy & Shashank S. Holla & Karthik Rao & Aditya Chandrasekar, 2024. "A Low-Complexity Solution for Optimizing Binary Intelligent Reflecting Surfaces towards Wireless Communication," Future Internet, MDPI, vol. 16(8), pages 1-15, July.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:8:p:272-:d:1446142
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    References listed on IDEAS

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    1. Ajmery Sultana & Xavier Fernando, 2022. "Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach," Future Internet, MDPI, vol. 14(9), pages 1-18, August.
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