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Various input huge-data-rate transmission through wireless communications are probable by Multiple Input Multiple Output (MIMO) matching by Orthogonal Frequency Division Multiplexing (OFDM) systems. The OFDM is affected majorly through high Bit Error Rate (BER) values in wireless communication. To overcome this problem, Adaptive Manta Ray Foraging Optimization (AMRFO) is proposed to attain less BER scores in pilot pattern design for Channel Estimation (CE). The MRFO is changed into AMRFO through integrating Adaptive Control Parameter (ACP) to enhance balance among exploration and exploitation. The ACP is integrated to determine foraging process to enhance search process. The AMRFO easily identify the possible local areas in global space through accurate and quick convergence. The Symbol Error Rate (SER), Normalized Mean Square Error (NMSE), and BER are taken as performance metrics for calculating AMRFO performance. The AMRFO obtains 0.2913 of SER, 0.0225 of NMSE, and 0.00046 of BER for Signal-to-Noise Ratio (SNR) of 10 dB when compared to Spatial Partitioning with Coalitional Game Theory (SPCGT).

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Adaptive Manta Ray Foraging Optimization-Based Pilot Pattern Design for Channel Estimation in Wireless Communication

  • 会议论文

  • 发表日期：

    2026年

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  • Ammar Hameed Shnain,
  • Z. Abed,
  • Errabelli Annapoorna

摘要

Various input huge-data-rate transmission through wireless communications are probable by Multiple Input Multiple Output (MIMO) matching by Orthogonal Frequency Division Multiplexing (OFDM) systems. The OFDM is affected majorly through high Bit Error Rate (BER) values in wireless communication. To overcome this problem, Adaptive Manta Ray Foraging Optimization (AMRFO) is proposed to attain less BER scores in pilot pattern design for Channel Estimation (CE). The MRFO is changed into AMRFO through integrating Adaptive Control Parameter (ACP) to enhance balance among exploration and exploitation. The ACP is integrated to determine foraging process to enhance search process. The AMRFO easily identify the possible local areas in global space through accurate and quick convergence. The Symbol Error Rate (SER), Normalized Mean Square Error (NMSE), and BER are taken as performance metrics for calculating AMRFO performance. The AMRFO obtains 0.2913 of SER, 0.0225 of NMSE, and 0.00046 of BER for Signal-to-Noise Ratio (SNR) of 10 dB when compared to Spatial Partitioning with Coalitional Game Theory (SPCGT).