The future wireless networks support multimedia applications and require ensuring quality of the services they provide. With increasing number of users, the radio resource is becoming scarce. Therefore, how should the demands for higher data rates with limited resources be met for Long Term Evolution-Advanced (LTE-A) is turning out to be a vital issue. In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multiobjective optimization (MOO) technique for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel Multiobjective Optimizer (MOZ) that selects an optimal solution out of a Pareto optimal (PO) set in accordance with the users QoS requirements. We then elaborate the scheduling process and prove through performance evaluation that use of MOO can provide potential solutions for solving the problems for resource allocation in the advancement of LTE-A networks. Simulations are carried out using LTE-Sim simulator, and the results reveal that MOZ outperforms the reference algorithm in terms of throughput guarantees, delay bounds, and reduced packet loss. Additionally, it is capable of achieving higher throughput and lower delay by giving equal transmission opportunity to all users and achieves 100% accuracy in terms of selecting optimal solution.
Adaptive radio resource management for maximizing reward and balancing loads in 4G hybrid universal mobile telecommunications system and long term evolution communications
