Clustering-Driven Optimization of RRH-BBU Assignment for Green Communication Networks with Big Data Analytics

Abstract

The Centralized Radio Access Networks (CRAN) decentralizes data and control planes by separating the baseband unit (BBU) from the central office, enabling energy-efficient "green networks"through the shutdown of underutilized BBUs. Analyzing extensive Call Detail Records (CDR) as big data, collected by service providers, has gained traction for extracting network features and studying activities. Thus, big data analytics are deemed as potential techniques that various research proposed to analyze the CDR. This paper introduces an energy-efficient CRAN network architecture based on the CRAN framework, focused on an innovative remote radio head (RRH)-BBU assignment. The objective is twofold: minimizing power consumption by deactivating underutilized BBUs and reducing inter-BBU handover rates based on CDR insights. In literature, the problem of assigning RRH to BBU is described as hard nonlinear programming (NLP) problem (bin packing, mixed integer), different suboptimal algorithms have been proposed to offer suboptimal assignment. This study employs clustering techniques to divide the complex NLP problem into simpler optimization tasks, achieving optimal RRH-BBU assignments. The proposed algorithm's effectiveness was assessed using Milan city CDR as a case study, and its performance was validated against Milan's land use map. The results indicated a remarkable 28.8% reduction in power consumption, alongside improvements in inter-BBU handovers.