International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Attention-Based Neural Beamforming Framework for Intelligent mmWave 6G Wireless Communication Systems

Author(s) Mr. Khalid ., Mr. Prashant Baghmar
Country India
Abstract Millimeter-wave (mmWave) communication and massive Multiple-Input Multiple-Output (MIMO) systems are considered key enabling technologies for next-generation 6G wireless networks due to their capability to provide high data rates and enhanced spectral efficiency. However, conventional beamforming approaches suffer from high computational complexity and beam alignment latency under dynamic wireless environments. To address these challenges, this paper proposes an attention-based neural beamforming framework for intelligent mmWave 6G communication systems. The proposed framework integrates geometric mmWave channel modeling, channel state information (CSI) extraction, and a self-attention based deep learning architecture for adaptive beam prediction and beamforming optimization. The self-attention mechanism efficiently captures dominant spatial channel characteristics, enabling accurate beam selection and interference suppression. Simulation results demonstrate that the proposed framework achieves improved beam prediction accuracy of approximately 98.6%, enhanced signal-to-interference-plus-noise ratio (SINR), higher spectral efficiency, reduced bit error rate (BER), and increased throughput compared to conventional and baseline deep learning beamforming approaches. Furthermore, the proposed framework significantly reduces computational complexity, making it suitable for real-time intelligent 6G wireless communication systems
Keywords Millimeter-Wave Communication, Massive MIMO, Beamforming, Deep Learning, Transformer Networks, Hybrid Beamforming, 6G Wireless Communication, Beam Prediction, Intelligent Wireless Networks
Field Engineering
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-29
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.79932
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E-ISSN 2582-2160
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