QoS- aware Multi-objective PSO-FA based Optimizer for Uplink Radio Resource Management of LTE-A Network

Long Term Evolution Advanced (LTE-A), is a leading-edge technology that provides higher peak data rates, more consumers per cell, lower control plane latency and delivers variety of Quality of Service (QoS) options which include Throughput, Packet Loss Rate, Packet Delay and the delay variation. The communication on the LTE air interface occurs between the Evolved Node B (eNB) and User Equipment (UE) and it is exceptionally imperative to deal with the resources because of the uninterrupted allocation of resources on the LTE networks. From the 3GPPLTE principles, while there is no coordination in uplink, at that juncture there would be a disputation based random access and it would be very tough administration would be a difficult job due to asymmetrical and mammoth UE transmissions. To surmount this challenge, a QoS-aware Multi-objective PSO-FA based Optimizer for Uplink Radio Resource Management of LTE-A Network is suggested in our research which eliminates the multi-objective Knapsack issue with limited modification in the class-based resource allocation scenario. The first phase of the work would be the allocation of resources to the users in the network by calculating the metric which is based upon QCI and priority function. The second phase of the work includes a QoS-aware optimizer with three objective functions, which can guarantee QoS service among users. Hybrid Optimizer is proposed in this work which integrates Firefly Algorithm (FFA) and Particle Swarm Optimization (PSO) by enhancing the fairness index and throughput.