Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramesh, Balasubramani; Sekhar, JampaniChandra; Marimuthu, Thangam; Vindhya, AbdulSatarShri Title A name given to the resource Improving Signal Coverage in Millimeter-Wave Massive MIMO via Efficient Predefined-Time Adaptive Neural NetworkBased Beam Training Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Communication Systems;Volume;38;Issue;13;Article No.;e70186; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/dac.70186" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/dac.70186</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011768039?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011768039?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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 &amp; Sons Ltd. Subject The topic of the resource beam training; deep learning; efficient predefined time adaptive neural network; fire hawk optimization algorithm; millimeter wave; signal coverage Publisher An entity responsible for making the resource available John Wiley and Sons Ltd Relation A related resource ISSN: 10745351; CODEN: IJCYE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Format The file format, physical medium, or dimensions of the resource online