Authors:
Shovon Nandi,Madhumita Sarkar,Arindam Sarkar,DOI NO:
https://doi.org/10.26782/jmcms.2026.03.00008Keywords:
Bit Error Rate,Channel Estimation,Channel State Information,Deep Learning,Inter-Carrier Interference,Mean Squared Error,MU-MIMO-OFDM,Peak Signal-to-Noise Ratio,Sparse Spatial Graph Neural Network,Abstract
The integration of Sparse Spatial Graph Neural Network (SSGNN) is a promising approach for enhancing the security and performance of Multiple User - Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing (MU-MIMO-OFDM) systems. SSGNN can effectively model the sparse channel structure and estimate the channel state information (CSI) in real-time. This research introduces AI-driven solutions for next-generation wireless systems, focusing on a Sparse Spatial Graph Neural Network (SSGNN) optimized with Fennec Fox Optimization (FFO) for secure multi-user MIMO-OFDM channel estimation and interference mitigation. The proposed SSNGN-FFO approach achieves exceptional performance, with a remarkably low Bit Error Rate (BER) of 0.00012 and a high Peak Signal-to-Noise Ratio (PSNR) of 45dB, indicating its potential for reliable and high-quality wireless communication using MATLAB.Refference:
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