Hysteretic Noisy Chaotic Neural Networks for Resource Allocation in OFDMA System

This paper addresses two-stage resource allocation in the orthogonal frequency division multiplexing access system. In the subcarrier allocation stage, hysteretic noisy chaotic neural network (HNCNN) with a newly established energy function is proposed for subcarrier allocation to improve the optimization performance and reduce the computational complexity. Activation functions with both anticlockwise and clockwise hysteretic loops are applied to the HNCNN. A new energy function is established for an objective function, which can be calculated offline, resulting in a lower computational complexity in solving subcarrier allocation than the previous energy function. In the power allocation stage, the water-filling algorithm is employed to attain optimal power allocation. Simulation results show that the energy function established in this paper can decrease the runtimes of the neural networks, and that the HNCNN with both anticlockwise and clockwise hysteretic-loop activation functions can improve probabilities of feasible and optimal solutions at higher noises. The two-stage algorithm in this paper outperforms the previous algorithms in fairness, system throughput, and resource utilization.