Green machine learning protocols for cellular communication

Abstract

Cellular networks with extensive topologies and intricate radio configurations are ideally suited for machine learning (ML), which is quickly becoming a popular method of adding intelligence. Based on the analysis of numerous network variables, machine learning (ML) is able to identify and assess network characteristics such as hotspots, interference distribution, congestion sites, traffic bottlenecks, and spectrum availability. Due to the large number of nodes and varying link quality, ML can evaluate extremely complicated cellular networks. The only thing that machines need to learn is how to autonomously collect spectrum information from the network and utilize that information to dynamically adjust the necessary wireless characteristics, such as frequency band, symbol modulation, coding rate, and route selection. Unquestionably, green machine learning will require more effective and efficient cellular communications than before. As a result, strategies like spectrum sharing, dynamic spectrum access, signal intelligence extraction, and effective routing are soon going to be crucial parts of the wireless communication paradigm for green ML. In this concept, Green ML seeks to bring the network to its best operational point. This chapter gives a thorough assessment of the state-of-the-art usage of green ML approaches to tackle key difficulties in cellular communications, in addition to the ad hoc networking element, due to the influence of green ML in cellular communications. With the development of green computing and the extensive use of networks, there is a severe cellular network growth network. Cellular networks are implemented when energy efficiency is a main goal, and they might be a key factor in helping every firm achieve sustainability.