Pruned Autoencoder based mmWave Channel Estimation in RIS-Assisted Wireless Networks

被引：0

作者：

Kim, Kitae ^{[1
]}

Hong, Choong Seon ^{[1
]}

机构：

[1] Kyung Hee Univ, Comp Sci & Engn, Yongin, South Korea

来源：

2022 23RD ASIA-PACIFIC NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (APNOMS 2022) | 2022年

基金：

新加坡国家研究基金会;

关键词：

Channel Estimation; Deep Learning; Reconfigurable Intelligent Surfaces; mmWave;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate channel estimation is an essential factor in determining the efficiency of a wireless communication system. Moreover, in Reconfigurable Intelligent Surfaces(RIS)-Assisted wireless networks using millimeter wave(mmWave), it is crucial to optimize each RIS element's phase shift. Therefore, in this paper, we propose a channel estimation method in TDD-based wireless communication system using autoencoder in RIS-Assisted wireless networks. The trade-off relationship between channel estimation accuracy and the number of pilot signals is optimized when performing channel estimation. Through denoising autoencoder and Average Percentage of Zeros(APoZ), we find the optimal pilot pattern considering not only the number of pilots but also the location. As a result of the experiment, the proposed method has little difference in performance or outperforms the full neural network without pruning.

引用

页码：327 / 330

页数：4

共 8 条

[1] Contribution of the Zubair source rocks to the generation and expulsion of oil to the reservoirs of the Mesopotamian Basin, Southern Iraq [J].