Hybrid-genetic-algorithm-based resource allocation for slow adaptive OFDMA system under channel uncertainty

A resource allocation algorithm for the slow adaptive orthogonal frequency division multiple access system under channel uncertainty is considered. The optimisation objective maximises the long-term system throughput over subcarrier assignment and the constraint condition satisfies the short-term data rate requirements of individual users, except occasional outage. Such an objective has a natural chance-constrained programming formulation. To solve the chance-constrained optimisation, the neural network and the genetic algorithm (GA) are integrated to develop a hybrid GA (HGA) which could satisfy the user data rate requirement with the target outage probability. The simulation tests verify that the HGA yields a higher long-term system throughput than the Li algorithm with the Bernstein approximation.