A Comprehensive Review on Machine Learning-based Approaches for Next Generation Wireless Network

A Comprehensive Review on Machine Learning-based Approaches for Next Generation Wireless Network

연구 분야: Infrastructure

학회: SN Computer Science

Rapid progress of wireless communication and networks plays a critical role in different applications related to carrying out heterogeneous ever-increasing traffic in multimedia communication, remote health, and commercial applications. The fastest developments and uninterrupted upgradations in recent telecommunication networks regarding the deployment of the advanced 5G, which is being rolled out, draw attention to various research challenges and demanding transition requirements from existing 4G to 5G, beyond (B5G) and toward the 6G. These network upgradations strongly stipulate the need for system flexibility along with intelligent decisions at run-time with the integration of Artificial Intelligence (AI) and Machine Learning (ML)-based techniques. In this paper, various research challenges regarding the rolling out of advanced 5G network, the need for artificial intelligence in the rapidly emerging next generation wireless network (NGWN) along with various state-of-the-art approaches of the Artificial Intelligence and Machine learning, target applications, challenges regarding the incorporation of intelligence in the network have been investigated in a unified way. The paper highlights the contemporary cutting-edge developments of the telecom industry, research trends of network edge intelligence including the integration of federated learning (FL), allied challenges and open research issues in promising 6G networks.