Robust Tensor-Based Algorithm for UAV-Assisted IoT Communication Systems via Nested PARAFAC Analysis

被引：8

作者：

Du, Jianhe ^{[1
]}

Luo, Xin ^{[1
]}

Jin, Libiao ^{[1
]}

Gao, Feifei ^{[2
]}

机构：

[1] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China

[2] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Inst Artificial Intelligence Tsinghua Univ THUAI, Dept Automat,State Key Lab Intelligent Technol &, Beijing 100084, Peoples R China

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2022年 / 70卷

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

Internet of Things; Signal processing algorithms; Symbols; Estimation; Channel estimation; Tensors; Autonomous aerial vehicles; CSI estimation; IoT; superimposed signal transmission; symbol detection; UAV; ENHANCED LINE SEARCH; CHANNEL ESTIMATION; MIMO COMMUNICATIONS; DECOMPOSITIONS; OPTIMIZATION; PERFORMANCE; RECEIVER; SYMBOL; RANK;

D O I：

10.1109/TSP.2022.3215637

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a robust tensor-based strategy to jointly estimate channel state information (CSI) and detect information symbols for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) communication systems. First, a superimposed signal transmission scheme is designed for each IoT device, and the superimposed signals are transmitted to UAVs simultaneously. Then, UAVs amplify and forward the received signals to the base station (BS), where a combined nested parallel factor (PARAFAC) tensor model is constructed. Finally, a robust tensor-based algorithm is derived to estimate full knowledge of CSI and detect information symbols. Simulation results show that the proposed algorithm offers superior performance compared with the competitive methods.

引用

页码：5117 / 5132

页数：16

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