Green machine learning approach for QoS improvement in cellular communications

Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due to the dynamic cellular network behaviors related to interference distribution, traffic bottlenecks, congestion points, and hotspots, there is a need to evaluate the dynamic processes in cellular systems in addition to ensuring spectrum availability. The delay, loss rate, and SNR are the most issues that may affect cellular communication performance. Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. It enables the extraction of spectrum knowledge from the network autonomously. The extracted information helps to know about every dynamic change in wireless parameters, related to frequency, modulation, route selection, etc. This paper provides details about the use of ML in green cellular networks to efficiently upgrade the communications and enhances different related approaches including quality of services (QoS), signal traffic load, and energy efficiency, which are critical issues of green cellular communication paradigms. The paper also presents the technical concept of green ML approaches to solve significant problems in cellular communications, in addition to future aspects and considerations for energy consumption minimization using the green ML approach in cellular radio communications