Hybrid Optimization Algorithm for Resource Allocation in LTE-Based D2D燙ommunication

Abstract

In a cellular network, direct Device-to-Device (D2D) communication enhances Quality of Service (QoS) in terms of coverage, throughput and amount of power consumed. Since the D2D pairs involve cellular resources for communication, the chances of interference are high. D2D communications demand minimum interference along with maximum throughput and sum rate which can be achieved by employing optimal resources and efficient power allocation procedures. In this research, a hybrid optimization model called Genetic Algorithm-Adaptive Bat Optimization (GA-ABO) algorithm is proposed for efficient resource allocation in a cellular network with D2D communication. Simulation analysis demonstrates that the proposed model involves reduced interference with maximum sum rate and throughput. The performance of the proposed model is compared with the existing Ant Colony Optimization-based resource exchange and GAME (ACO-GAME) theory models, Trader-assisted Resource EXchange mechanism-Radio Access Network (TREX-RAN) and De-centralized Radio Access Network (TREX-DRAN), and greedy CYcle-Complete preferences (CYC) models. The proposed model offers a maximum sum rate of 83 kB/s, which is much better than the existing techniques.