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 Nancy Lima Christy, S.; Rajasekaran, N.; Kamalaveni, A.; Shavkatov, Navruzbek; Rabiyathul Fathima, M.; Md. Zubair Rahman, A.M.J. Title A name given to the resource Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels 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 National Journal of Antennas and Propagation;Volume;7;Issue;1;pp.297-306 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.31838/NJAP/07.01.33" target="_blank" rel="noreferrer noopener">https://doi.org/10.31838/NJAP/07.01.33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013146603?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013146603?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 Nancy Lima Christy S., Department Of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Tamil Nadu, Chennai, India; Rajasekaran N., Department of Computer Science Christ (Deemed to be University), Karnataka, Bengaluru, India; Kamalaveni A., J.K.K Nataraja College of Arts and Science, Kumarapalayam (TK), Namakkal (DT), Tamil Nadu, India; Shavkatov N., Department of Corporate Finance and Securities, Tashkent State University of Economics, Tashkent, Uzbekistan; Rabiyathul Fathima M., Department of Information Technology, Al-Ameen Engineering College, Tamil Nadu, Erode, India; Md. Zubair Rahman A.M.J., Department of Electronics and Communication Engineering, Al-Ameen Engineering College, Tamil Nadu, Erode, India Description An account of the resource Dependable signal detection has also been a major concern in real-time wireless communication especially in the case of fading channels that cause non-adaptive distortion and deteriorate the overall performance drastically. The conventional detection meth-ods, like the maximum likelihood detection, are not always adaptive in the circumstances of dynamic and therefore unpredictable channel conditions, and particularly in the cases when the statistical profiles are unknown or vary too quickly. In order to address these shortcomings, the papers introduce a new paradigm of deep learning signal detection trained to learn hierarchies and temporal patterns of raw received signals, which by their pas integrate convolutional neural networks (CNN) and recurrent neural networks (RNN). The trained architecture is end-to-end that is able to map the noisy distorted inputs to their symbols which are inherently transmitted in the context of channel state informa-tion. Heavy simulation over Rayleigh and Rician fading channels with different Doppler spreads and SNR values shows that the suggested approach shows substantial improve-ment over the traditional maximum likelihood and classical machine learning-based detec-tors regarding bit error rate (BER), inference latency and computational overhead. Such results emphasize the performance as well as the flexibility of deep learning model in very dynamic propagation conditions. On the whole, this paper draws the conclusion that deep learning is a perspective direction to solve the problem of real-time detection of a signal in next-generation wireless networks, such as a 6G or IoT edge setup. 2025, Society for Communication and Computer Technologies. All rights reserved. Subject The topic of the resource Deep learning; Fading channels; Neural networks; Real-time communication; Signal detection; Wireless propagation Publisher An entity responsible for making the resource available Society for Communication and Computer Technologies Relation A related resource ISSN: 25822659; 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; Gold Open Access Format The file format, physical medium, or dimensions of the resource online