Call Admission Control Decision Maker Based on Optimized Fuzzy Inference System for 5G Cloud Radio Access Networks

Fifth generation (5G) cell frameworks are relied upon to encounter colossal traffic congestion from mobile devices. To decrease network congestion, the 5G cell systems need to be changed to accommodate the soaring traffic demands from these devices. In a 5G network, a cloud-based radio access network (C-RAN) has a significant role to increase the data rate. With the support of C-RAN, traffic congestion in the 5G network is handled by presenting the Call Admission Control (CAC) technique. Although this CAC technique improves the system efficiency, maximum of call blocking probability due to the traffic congestion is the main challenge. So, the solution to this challenge is introducing the optimal call admission decision maker. In this paper, Artificial Fish Swarm Algorithm based Fuzzy Inference System (FIS-AFSA) is proposed as a decision maker. Using AFSA algorithm, the fuzzy parameters are optimized in this strategy. In this approach, few delay tolerant connections are outsourced from private cloud to public cloud with certain price when congestion. The proposed FIS-AFSA based CAC scheme's performance outperforms that of the Fuzzy based CAC technique in the basis of call blocking probability, throughput and resource utilization as show in simulation results.