Vision, Challenge, Futuristic Approach of Machine Learning in 6G Wireless Technology

Abstract

In recent years, the machine learning approach has been a major surge of research interest in intelligence, data-driven advanced dynamic approaches, and automation of low-powered gadgets. The goal for 6G is a massively linked complex network capable of reacting quickly to user service requests by real-time learning of network status as given by the network edge, user-side, and air interface. Most machine learning application 6G vehicular networks are intended to generate a rapidly evolving and intelligent system that allows networks to adapt to various application requirements and service types. In the autonomous driving age of 6G, ML is used to design a route, and 6G's improvements in response time, roaming range, network energy efficiency, and connectivity provide a better experience for users. Scholars are afraid about this problem due of privacy concerns. Overall, the chapter includes the basic and advanced levels of machine learning including the unsupervised, supervised, and reinforced learning and the challenges of ML for 6G.