Proportional fair resource allocation based on hybrid ant colony optimization for slow adaptive OFDMA system

Abstract

Proportional fair resource allocation plays a critical role to enhance the performance of slow adaptive orthogonal frequency division multiple access (OFDMA) system. In slow adaptive OFDMA system, the subcarrier allocation is updated at the beginning of every time window and the channel gain of users at the time of the subcarrier allocation could not be known precisely. This leads to the challenge for designing the resource allocation algorithm for slow adaptive OFDMA system. In this work, we formulate a proportional fair resource allocation problem based on chance-constrained programming for slow adaptive OFDMA system which maximizes the average sum-rate in an adaptive time window and guarantees the Jain fair index (JFI) requirement with the target probability. In order to solve the chance-constrained resource allocation problem, we propose hybrid ant colony optimization (HACO) which combines ant colony optimization (ACO) and support vector machine (SVM). Simulation results demonstrate that HACO not only yields higher average sum-rate, but also guarantees the chance-constrained condition very well compared with other algorithm.