Deep Learning for Joint Pilot Design and Channel Estimation in Symbiotic Radio Communications

被引：11

作者：

Wang, Ze ^{[1
]}

Xu, Hongbo ^{[1
]}

Zhao, Li ^{[1
]}

Chen, Xue ^{[1
]}

Zhou, Aizhi ^{[1
]}

机构：

[1] Cent China Normal Univ, Dept Elect & Informat Engn, Wuhan 430079, Peoples R China

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2022年 / 11卷 / 10期

关键词：

Channel estimation; Backscatter; Modulation; Deep learning; Internet of Things; Interference cancellation; Symbols; Symbiotic radio (SR); channel estimation; pilot design; deep learning; NETWORKS; INTERNET; SYSTEMS; THINGS;

D O I：

10.1109/LWC.2022.3193093

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Symbiotic radio (SR), a new green communication technology, is proposed to address staggering amount of energy and spectrum resource requirements from massive Internet of Things (IoT) devices, where some passive backscatter devices form backscatter-link transmissions. The aim of this letter is to solve the pilot design and channel estimation issues for the SR system. To this end, we propose a novel joint pilot design and channel estimation (JPDCE) scheme based on deep learning, which consists of the pilot designer and channel estimator. Moreover, deep residual network and the successive interference cancellation (SIC) technique are used to eliminate the noise and the interference between different channels, respectively. Numberous simulation results show that the proposed scheme has superior performance of channel estimation.

引用

页码：2056 / 2060

页数：5

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