Machine Learning Algorithms for 6G Wireless Networks
Anita Patil, Sridhar Iyer, Rahul Jashvantbhai Pandya
Abstract
Over the past decade, in view of minimizing network expenditures, optimizing network performance, and building new revenue streams, wireless technology has been integrated with artificial intelligence/machine learning (AI/ML). Further, there occurs dramatic minimization of power consumption and improvement in system performance when traditional algorithms are replaced with deep learning-based AI techniques. Implementation of ML algorithms enables wireless networks to advance in terms of offering high automation levels from distributed AI/ML architectures applicable at network edge and implementing application-based traffic steering across access networks. This has enabled dynamic network slicing for addressing different scenarios with varying quality of service requirements and has provided ubiquitous connectivity across various 6G communication platforms. Keeping a view of the aforementioned, in this chapter, the authors present a survey of various ML techniques that are applicable to 6G wireless networks. They also list open problems of research that require timely solutions.