NOMA Codebook Optimization by Batch Gradient Descent

被引：8

作者：

Si, Zhongwei ^{[1
]}

Wen, Shaoguo ^{[1
]}

Dong, Bing ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing 100876, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

中国国家自然科学基金;

关键词：

Batch gradient descent; codebook optimization; neural network; non-orthogonal multiple access; pairwise error probability;

D O I：

10.1109/ACCESS.2019.2936483

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The non-orthogonal multiple access (NOMA) has the potential to improve the spectrum efficiency and the user connectivity compared to the orthogonal schemes. The codebook design is crucial for the the performance of the NOMA system. In this paper, we comprehensively investigate the NOMA codebook design involving the characteristics from multiple signal domains. The minimizing of the pairwise error probability is considered as the target of the optimization. The neural network framework is explored for the optimization, and the mapping functions on the edges are considered as weights. The method of batch gradient descent is applied for optimizing the weights and correspondingly the codebook. The simulation results reveal that with the optimized codebook the error performance is significantly improved compared to the schemes in the literature.

引用

页码：117274 / 117281

页数：8

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