Next Generation Wireless Networks: Optimisation and Resource Allocation Using Machine Learning

Abstract

The emergence of 5G and the upcoming 6G wireless networks set the foundation for a new paradigm of wireless communications to offer very high data rate, huge number of devices and very low latency. But to fine-tune resource utilization as well as augment the capability of the network within such settings remains a difficult feat on its own. This research explores how it is possible to solve these issues through the use of new generation of machine learning (ML) techniques; dynamic spectrum management, interference cancellation, and energy management.  In our research we investigate different types of the ML approaches, such as deep, reinforcement, and federated learning to design smart resource allocation frameworks. These frameworks are envisaged to be self-configurable to adapt to the variations in the network, demanded traffic and interference thereby achieving higher spectrally efficiency and low energy utilization. Real-time data analysis is an important part of our study; the research also uses predictive mathematical techniques to determine the ideal distribution of resources based on the forecast of network traffic.  We also study the real-time application of ML-based interference management measures including but not limited to, beam forming and dynamic power control to improve signal quality and minimize cross-system interference. They are tested and evaluated using detailed simulations and actual test beds where the enhancements in the throughput, delay and reliability of the networks are actualized.  By focusing on the enhanced applicability of machine learning in defining the new characteristics of next-generation wireless networks, this research underlines the capacity of novel technologies in making the future communication systems more efficient, reliable, and environmentally friendly. These findings are well useful for the network operators, policymakers, and researchers, and bring out the significance of AI in the development of the next generation of wireless communication.