Intelligent Dynamic Resource Management In The Cloud Radio Access Networks (C-RAN) With Bio-inspired Algorithms
Abstract
Cloud Radio Access Networks (C-RANs) has been proposed as an effective solution for supporting the fifth generation (5G) requirements by achieving centralized resource management, costs reduction, increasing energy efficiency, spectral efficiency, and cooperative processing by aggregating Baseband Units (BBUs) in a centralized cloud Center. However, the high-density deployment of small cells supporting macro cells increases the resource management difficulty, making it a NP-hard optimization problem that requires suitable algorithms to address the problem nature while ensuring quality of service (QoS). Given the flexibility of Bio-inspired Algorithms and their ability to adapt to traffic demand changes, we study one such algorithm, namely the hybrid SA-PSO algorithm composed of two stages: a dynamic Active Remote Radio Heads (RRHs) selection using the Simulated Annealing (SA) algorithm, and a second stage where the Particle Swarm Optimization (PSO) algorithm forms clusters of RRHs and connects them to cloud BBU pool to ensure load balancing between these units. We take into consideration the dynamic changes in users traffic domain over 24 hours to study the Tidal effects. Simulation results show that the SA-PSO algorithm outperforms the Heuristic Dynamic Switch Off (HDSO) algorithm in Average number of Active RRHs by 13.81% in low traffic periods and 14.36% during peak hours, while maintaining the required QoS.
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