OFDMA系统中的子载波和功率联合TCNN优化

Joint Subcarrier and Power Optimization in OFDMA Systems Based on Transiently Chaotic Neural Network

提出了一种自适应的多用户正交频分多址(OFDMA)系统中的资源优化算法.算法包括2步:首先在功率平均分配的前提下采用暂态混沌神经网络(TCNN)进行子载波分配.然后对平均分配的功率利用线性注水算法进行重新分配.仿真结果表明,TCNN在收敛速度和最优化率方面都比Hopfield神经网络和混沌神经网络有明显的改善.与传统的资源分配算法比较,所提出的算法能更加充分地挖掘多用户分集增益,进一步提高了系统总的吞吐量.

An adaptive resource optimization algorithm for multiuser orthogonal frequency division multiple access （OFDMA） system is proposed. The algorithm consists of two steps： first, subcarriers are assigned by transiently chaotic neural network （TCNN） when the power is assumpted to be divided equally to every subcarrier. Second, the averagely assigned power is reallocated in a linear water-filling fashion. Simulations show that TCNN has better convergence speed and optimization rate than Hopfield neural network and chaotic neural network. Compared with existing algorithms, the proposed algorithm further achieves higher throughput by exploiting the multiuser diversity gain.