ItemArticle

Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels

Title
Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels
Creator
Nancy Lima Christy, S.; Rajasekaran, N.; Kamalaveni, A.; Shavkatov, Navruzbek; Rabiyathul Fathima, M.; Md. Zubair Rahman, A.M.J.
Description
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.
Source
National Journal of Antennas and Propagation;Volume;7;Issue;1;pp.297-306
Date
01-01-2025
Publisher
Society for Communication and Computer Technologies
Subject
Deep learning; Fading channels; Neural networks; Real-time communication; Signal detection; Wireless propagation
Coverage
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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 25822659;
Format
online
Language
English
Type
Article

Citation

Nancy Lima Christy, S.; Rajasekaran, N.; Kamalaveni, A.; Shavkatov, Navruzbek; Rabiyathul Fathima, M.; Md. Zubair Rahman, A.M.J., “Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23489.

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