Constriction Factor Particle Swarm Optimization based load balancing and cell association for 5G heterogeneous networks

With the rapidly growing traffic demand, the cellular industry is moving toward heterogeneity to fulfill the heavy traffic requirement. The existing heterogeneous networks are comprised of the Long-Term Evolution (LTE), LTE-Advanced (LTE-A), and other compatible access technologies. It has been identified that due to the shortage of capacity, cell performance can deteriorate. To deal with users’ requirements, the 5G heterogeneous network architectures comprising LTE-advanced access technology with the combination of comparatively low configured small base stations, and macro eNodeBs (MeNB) have been extended as a solution. Therefore, operators have introduced the 5G with the existing LTE Advanced Heterogeneous Network (5GLHN) where small cells such as Home eNodeBs (HeNBs) are deployed overlapping with the conventional macro eNodeB (MeNB). However, in highly dense and closely compacted 5GLHNs, the cell capacity can still be lower than what is on-demand, thus affecting the system throughput. This article proposes a framework to maximize the throughput in 5GLHNs through a Constriction Factor Particle Swarm Optimization (CFPSO) technique of cell association and load-balancing algorithm to enhance the throughput of the 5GLHN. The proposed approach is designed to offload the traffic of MeNB Users (MUEs) to the small cells (HeNBs). The convergence, cumulative distribution function of the UEs rate, average throughput, and allocation time are analyzed to evaluate the performance of the proposed CFPSO approach. Simulation results reveal that the throughput of the proposed approach is improved by up to 44.08% of the existing index-based approach and by 94.20 \% of the existing Matching with Minimum Quota (MMQ) approach