AI-Driven Signal Processing Techniques For Next-Generation Communication And Smart Systems

Dr. K. Sundravadivelu; Dr. A. Sasi Kumar; Dr. S. Selvaraj; Neeta Nathani; Dr. Rahul Kumar; Dr Deepak A. Vidhate

doi:10.29284/p963x808

Authors

Dr. K. Sundravadivelu Assistant Professor, Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu
Dr. A. Sasi Kumar Associate Professor, School of Science and Computer Studies, CMR University, Bangalore, Bangalore Urban District, Karnataka
Dr. S. Selvaraj Assistant Professor, School of Science and Computer Studies, CMR University, Bangalore, Bangalore Urban District, Bangalore, Karnataka
Neeta Nathani Professor, Electronics & Communication Engineering, Gyan Ganga Institute of Technology & Sciences Jabalpur, Madhya Pradesh (M.P.)
Dr. Rahul Kumar Assistant Professor, Amity School of Businness, Amity University Jharkhand, Ranchi, Jharkhand
Dr Deepak A. Vidhate Professor & HOD IT, Department of Information Technology, Dr. Vithalrao Vikhe Patil College of Engineering Ahilyanagar, Maharashtra

DOI:

https://doi.org/10.29284/p963x808

Keywords:

Artificial Intelligence, Signal Processing, Machine Learning, Next-Generation Communication, Smart Systems, Wireless Networks, Intelligent Signal Analysis.

Abstract

The new reality of digital communication networks, Internet of Things (IoT) systems, and smart cyber-physical systems have led to the development of the most essential requirement of new signal processing instruments with the ability to operate large volumes of information in real time. Standard signal processing algorithms with a heavy dependence on deterministic mathematical models and set algorithms do not necessarily have the ability to react to varying conditions of communication including, noise, interference, bandwidth, or non-regular shapes of the signal. Over the last few years, artificial intelligence (AI) and machine learning algorithms are being applied as powerful tools to enhance signal processing capabilities in the next-generation communication and smart technology networks. Signal processing AI is an amalgamation of deep learning algorithms, adaptive neural net and smart pattern recognition to interpret and analyze signals that are being transmitted within communication networks automatically and to optimize them. It is possible that such technologies can be able to support communication systems that can dynamically respond to the different channel conditions, improve accuracy of signal detection, minimize interferences and maximize spectrum use. The joint application of AI and advanced signal processing algorithms has acquired a special importance in the new technologies of 5G and 6G wireless systems, autonomous systems, smart cities, and intelligent sensing platforms. The paper explores the reasons behind why AI-based signal processing applications will come in handy in helping to establish effective communication and smart system operation in the next-generation technology world. The proposed analysis algorithm integrates machine learning algorithms with digital signal processing algorithms to increase signal classifications, reduction of noise, adaptive filtering, and dependability of communication in the complex conditions of the network. The research is premised on the fact that it is multidisciplinary in its approach to the formulation of concepts associated with communication engineering, artificial intelligence, and data analytics to gauge the efforts of the AI-based signal processing systems. It has been demonstrated empirically that AI-based techniques are highly beneficial in enhancing the efficiency of the network, the accuracy of signal detection, and the smart decision-making in communication infrastructure. The paper also examines some of the problems that are associated with AI-enabled signal processing systems including computational complexity, models interpretability, and problems of data security. Overall, the findings prove that with the combination of artificial intelligence and signal processing models, the productivity and versatility as well as smarter character of the future communication systems and intelligent technological systems can be significantly improved.

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