Decentralized Renewable Resource Redistribution and Optimization for Beyond 5G Small Cell Base Stations: A Machine Learning Approach

Abstract

Optimal resource provisioning and management of the next generation communication networks are crucial for attaining a seamless quality of service with reduced environmental impact. Considering the ecological assessment, urban and rural telecommunication infrastructure is moving toward deploying green cellular base stations to cater to the needs of the ever-growing traffic demands of heterogeneous networks. In such scenarios, the existing learning-based renewable resource provisioning methods lack intelligent and optimal resource management at the small cell base stations (SCBS). Therefore, in this article, we present a novel machine learning-based framework for intelligent resource provisioning mechanisms for micro-grid connected green SCBSs with a completely modified ring parametric distribution method. In addition, an algorithmic implementation is proposed for prediction-based renewable resource re-distribution with energy flow control unit mechanism for grid-connected SCBS, eliminating the need for centralized hardware. Moreover, this modeling enables the prediction mechanism to estimate the future on-demand traffic provisioning capability of SCBS. Furthermore, we present the numerical analysis of the proposed framework showcasing the systems’ ability to attain a balanced energy convergence level of all the SCBS at the end of the periodic cycle, signifying our model’s merits.