AI-Assisted Beamforming Optimization in RIS-Enabled 6G Wireless Networks under Rayleigh Fading Channels

Authors

  • Mojtaba Nasehi * Professor, Faculty of Electrical Engineering, Isfahan Azad University, Isfahan, Iran
  • mehdi moradi

https://doi.org/10.22105/scfa.vi.91

Abstract

Reconfigurable Intelligent Surfaces (RISs) have recently emerged as a promising technology for enhancing the performance of Sixth-Generation (6G) wireless networks by enabling intelligent control of the radio propagation environment with low energy consumption and reduced hardware complexity. However, optimizing RIS phase-shift configurations remains a challenging non-convex problem, particularly in dynamic fading environments. To address this issue, this paper proposes an Artificial Intelligence (AI)-assisted beamforming optimization framework for RIS-enabled 6G wireless networks operating under Rayleigh fading channels. The proposed system employs a Deep Neural Network (DNN) to learn the relationship between Channel State Information (CSI) and optimal RIS phase configurations, enabling fast and efficient beamforming decisions without the computational burden of conventional iterative optimization algorithms. A comprehensive simulation framework is developed in MATLAB to evaluate the performance of the proposed architecture and compare it with conventional wireless communication systems and traditional RIS-assisted schemes. Performance is assessed in terms of Bit Error Rate (BER), Spectral Efficiency (SE), Energy Efficiency (EE), Channel Capacity, and Computational Complexity. The simulation results demonstrate that the proposed AI-RIS framework significantly improves communication reliability and spectral utilization while reducing optimization latency by more than 90% compared with conventional optimization approaches. Furthermore, the proposed method achieves superior BER performance, higher capacity, and enhanced energy efficiency under various Signal-to-Noise Ratio (SNR) conditions and RIS configurations. These findings confirm that the integration of AI and RIS technologies can effectively address the challenges of future intelligent wireless environments and provide a practical foundation for AI-native 6G communication systems.

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Published

2026-08-11

Issue

Articles in Press

Section

Articles

How to Cite

Nasehi, M., & moradi, mehdi. (2026). AI-Assisted Beamforming Optimization in RIS-Enabled 6G Wireless Networks under Rayleigh Fading Channels. Soft Computing Fusion With Applications . https://doi.org/10.22105/scfa.vi.91