Paper Title: A Review on Network Embedding Cognitive Radio Environment with Machine Learning Techniques for 5G and Beyond

Author's name: K.Babu, Dr.N.P.Ananthamoorthy, Dr.R.Rajeshkanna

Wireless communication systems are indispensable in today's society, serving a multitude of purposes ranging from entertainment and business to commercial, health, and safety applications. These systems undergo continuous evolution, with the current focus on the widespread implementation of fifthgeneration (5G) technology globally. However, discussions within academia and industry are already underway regarding the future of wireless communication systems beyond 5G, envisioning the advent of sixth-generation (6G) networks.A pivotal aspect of these forthcoming 6G systems will be the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. AI and ML will permeate every facet and layer of wireless systems, building upon the foundations laid by previous generations up to 5G. This comprehensive review aims to explore the conceptual framework of 6G and delineate the pivotal role of ML techniques across its various layers.Examining classical and contemporary ML methodologies, including supervised and unsupervised learning, Reinforcement Learning (RL), Deep Learning (DL), and Federated Learning (FL), this paper elucidates their relevance in the context of wireless communication systems. By providing insights into the utilization of ML techniques within each layer of the proposed 6G model, this review contributes to understanding the symbiotic relationship between ML and future wireless technologies. Furthermore, the paper outlines potential future applications and identifies research challenges in leveraging ML and AI for the advancement of 6G networks. Through this exploration, we aim to provide a road map for harnessing the transformative potential of ML in shaping the future landscape of wireless communication systems.

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