Intelligent Channel Learning Exploiting Practical Energy Harvesting for Wireless MISO Systems

作     者：Bhattacharya, Pritha Agarwal, Anirudh Verma, Monica 

作者机构：LNM Inst Informat Technol Dept Elect & Commun Engn Jaipur 302031 Rajasthan India 

出 版 物：《IEEE SYSTEMS JOURNAL》 (IEEE Syst. J.)

年 卷 期：2022年第16卷第4期

页      面：5879-5882页

核心收录：

学科分类：0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Decoding Channel estimation Training Wireless communication Signal to noise ratio MISO communication Deep learning Channel estimation (CE) deep autoencoder mean square error (mse) practical energy harvesting (EH) wireless power transfer (WPT) 

摘      要：This article proposes a novel practical energy harvesting (EH) model-assisted deep learning framework for intelligent channel tracking. Specifically, a multiantenna wireless system is considered for energy beamforming in a nonlinear model-based EH scenario. Deep autoencoder technique is utilized for learning the channel characteristics due to nonconvexity of the channel estimation optimization problem. The performance evaluation is validated in low signal-to-noise ratio regimes, thereby providing key optimal design insights. Numerical results depict an overall performance enhancement as compared with existing benchmarks.