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<p>Reconfigurable intelligent surfaces (RIS) are poised to revolutionize 6G communication systems by manipulating wave propagation for enhanced capacity and coverage. However, optimal RIS operation hinges on accurate channel state information (CSI), a challenge under real-world impulsive noise conditions where conventional methods falter. This paper proposes a novel correntropy-based stochastic gradient ascent (CSGA) learning algorithm for robust CSI estimation in RIS systems plagued by impulsive noise. Our CSGA method demonstrably outperforms existing techniques, leading to a significant improvement in the communication system’s average bit error rate (BER). This paves the way for reliable RIS operation in future 6G networks.</p>

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Robust channel estimation for reconfigurable intelligent surfaces in presence of impulsive noise

  • 期刊论文

  • 发表日期：

    2025年8月24日

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  • Mojtaba Hajiabadi,
  • Naaser Neda

摘要

Reconfigurable intelligent surfaces (RIS) are poised to revolutionize 6G communication systems by manipulating wave propagation for enhanced capacity and coverage. However, optimal RIS operation hinges on accurate channel state information (CSI), a challenge under real-world impulsive noise conditions where conventional methods falter. This paper proposes a novel correntropy-based stochastic gradient ascent (CSGA) learning algorithm for robust CSI estimation in RIS systems plagued by impulsive noise. Our CSGA method demonstrably outperforms existing techniques, leading to a significant improvement in the communication system’s average bit error rate (BER). This paves the way for reliable RIS operation in future 6G networks.