ItemArticle

Improving Signal Coverage in Millimeter-Wave Massive MIMO via Efficient Predefined-Time Adaptive Neural NetworkBased Beam Training

Title
Improving Signal Coverage in Millimeter-Wave Massive MIMO via Efficient Predefined-Time Adaptive Neural NetworkBased Beam Training
Creator
Ramesh, Balasubramani; Sekhar, JampaniChandra; Marimuthu, Thangam; Vindhya, AbdulSatarShri
Description
This paper proposes an advanced deep learning framework for efficient beam training in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To overcome the limitations of conventional beam training approaches such as high overhead, slow adaptation to dynamic environments, and poor scalability, an Improving Signal Coverage in Millimeter Wave Massive MIMO via Efficient Predefined Time Adaptive Neural Network based Beam Training (ISC-MMIMO-EPTANN-BT) model is proposed. The proposed model used deep neural network (DNN) to learn complicated nonlinearities in channel power leakage (CPL) and used an efficient predefined time adaptive neural network (EPTANN) to provide real-time responsiveness and temporal synchronism in beam training. The parameters of the model are also optimized using fire hawk optimization algorithm (FHOA) to get better convergence speed and signal coverage. The proposed technique is executed in MATLAB. The proposed approach attains better performance under successful rate by significantly less beam training overhead and also increases signal coverage based on simulation results. The proposed ISC-MMIMO-EPTANN-BT method attains 26.15%, 21.08%, and 33.75% higher successful rates and 16.32%, 28.94%, and 20.24% lower normalized mean square error compared with existing methods such as deep learning for beam training in millimeter wave massive MIMO schemes (BT-MMIMO-DNN), deep learning for combined feedback and channel prediction in large-scale MIMO systems (CNN-JCS-MMIMO), and triple-refined hybrid-field beam training in mmWave extremely large-scale MIMO (TR-FBT-MIMO), respectively. The ISC-MMIMO-EPTANN-BT technique reduced beam training overhead, enhanced signal coverage, and identified a promising candidate for successful beam training in mmWave massive MIMO schemes. 2025 John Wiley & Sons Ltd.
Source
International Journal of Communication Systems;Volume;38;Issue;13;Article No.;e70186;
Date
01-01-2025
Publisher
John Wiley and Sons Ltd
Subject
beam training; deep learning; efficient predefined time adaptive neural network; fire hawk optimization algorithm; millimeter wave; signal coverage
Coverage
Ramesh B., Department of Electronics and Communication Engineering, Annapoorana Engineering College, Tamil Nadu, Salem, India; Sekhar J., Department of Computer Science and Engineering, NRI Institute of Technology, Andhra Pradesh, Guntur, India; Marimuthu T., Department of Computer Science Engineering, School of Computing, Kalasalingam Academy of Research and Engineering (KARE), Deemed to be University, Tamil Nadu, Virudhunagar District, India; Vindhya A., Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, India
Rights
All Open Access; Bronze Open Access
Relation
ISSN: 10745351; CODEN: IJCYE
Format
online
Language
English
Type
Article

Citation

Ramesh, Balasubramani; Sekhar, JampaniChandra; Marimuthu, Thangam; Vindhya, AbdulSatarShri, “Improving Signal Coverage in Millimeter-Wave Massive MIMO via Efficient Predefined-Time Adaptive Neural NetworkBased Beam Training,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/21768.

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