Abstract
Orthogonal frequency division multiple access (OFDMA) is adopted in 4G wireless communication standard, where bandwidth resources can be split into smaller granular units. Tailored for slow adaptive OFDMA system, we study the energy-efficient resource allocation problem based on chance-constrained programming. The optimization objective is to minimize the power consumption over subcarrier and power allocation. The constraints include the outage probability constraint and target bit error rate (BER) constraint. Because the optimization problem contains the probabilistic constraint, it is a chance-constrained programming problem. Hence, support vector machine (SVM) is adopted to compute the outage probability constraint. Then, we integrate SVM and genetic simulated annealing (GSA) to develop hybrid genetic simulated annealing (HGSA). Simulation tests verify that HGSA not only has the lower consumption power, but also satisfies the outage probability constraint.
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