Differential evolution aided adaptive resource allocation in OFDMA systems with proportional rate constraints

Abstract

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA systems can be maximized by adaptively assigning subcarriers to the user with the best gain for that subcarrier, with power subsequently distributed by water-filling. In this paper, we propose the use of a differential evolution combined with multi-objective optimization (CMODE) algorithm to allocate the resources to the users in a downlink OFDMA system. Specifically, we propose two approaches for resource allocation in downlink OFDMA systems using CMODE algorithm. In the first approach, CMODE algorithm is used only for subcarrier allocation (OSA), while in the second approach, the CMODE algorithm is used for joint subcarrier and power allocation (JSPA). The CMODE algorithm is population-based where a set of potential solutions evolves to arrive at a near-optimal solution for the problem under study. During the past decade, solving constrained optimization problems with evolutionary algorithms has received considerable attention among researchers and practitioners. CMODE combines multi-objective optimization with differential evolution (DE) to deal with constrained optimization problems. The comparison of individuals in CMODE is based on multi-objective optimization, while DE serves as the search engine. In addition, infeasible solution replacement mechanism based on multi-objective optimization is used in CMODE, with the purpose of guiding the population towards the promising solutions and the feasible region simultaneously. It is shown that both the proposed approaches obtain higher sum capacities as compared to that obtained by previous works, with comparable computational complexity. It is also shown that the JSPA approach provides near optimal results at the slightly higher computational cost.