Novel Bee Colony Optimization with Update Quantities for OFDMA Resource Allocation

Abstract

In recent years, large usage of wireless networks puts forward challenge to the utilization of spectrum resources, and it is significant to improve the spectrum utilization and the system sum data rates in the premise of fairness. However, the existing algorithms have drawbacks in efficiency to maximize the sum data rates of orthogonal frequency division multiple access (OFDMA) systems in the premise of fairness threshold. To address the issue, a novel artificial bee colony algorithm with update quantities of nectar sources is proposed for OFDMA resource allocation in this paper. Firstly, the population of nectar sources is divided into several groups, and a different update quantity of nectar sources is set for each group. Secondly, based on the update quantities of nectar sources set for these groups, nectar sources are initialized by a greedy subcarrier allocation method. Thirdly, neighborhood searches and updates are performed on dimensions of nectar sources corresponding to the preset update quantities. The proposed algorithm can not only make the initialized nectar sources maintain high levels of fairness through the greedy subcarrier allocation but also use the preset update quantities to reduce dimensions of the nectar sources to be optimized by the artificial bee colony algorithm, thereby making full use of both the local optimization of the greedy method and the global optimization of the artificial bee colony algorithm. The simulation results show that, just in the equal-power subcarrier allocation stage, the proposed algorithm can achieve the required fairness threshold and effectively improve the system sum data rates.

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