Subcarrier Allocation for OFDMA Systems by Using Deep Neural Networks

• Main Authors: Wei-Jen Chen, 陳薇任
• Other Authors: Yung-Fang Chen
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/en3y9h

碩士 === 國立中央大學 === 通訊工程學系 === 107 === In this paper, we propose a deep neural networks (DNN) structure to allocate subcarrier for orthogonal frequency-division multiple access (OFDMA). Assuming that the channel gains of all subcarriers are known, and allocate to different number of users respectively. The proposed method can be dramatically increased the efficiency. We trying to minimize the mean squared error (MSE) between ESA algorithm while satisfying the bit error rate constraint. We suggest a deep learning architecture in which each group of allocation as a batch, after an appropriate number of iterations and epochs, the loss will tend to converge to a constant value. We also discuss different optimizer to compare their convergence rate. The proposed scheme offers better efficiency of allocating subcarrier and the performance is close to ESA algorithm.