Low-rank tensor recovery for topological interference management in time-varying networks

被引：0
|
作者：
Jiang, Xue [1 ]
Zheng, Baoyu [2 ]
Wang, Lei [2 ]
Hou, Xiaoyun [2 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing 210003, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Sch Telecommun & Informat Engn, Nanjing 210003, Peoples R China
来源：
DIGITAL SIGNAL PROCESSING | 2023年 / 133卷
基金：
中国国家自然科学基金;
关键词：
Low -rank matrix completion; Topological interference management; Time -varying networks; Low -rank tensor recovery; Tensor nuclear norm; FRAMEWORK; DESIGN;
D O I：
10.1016/j.dsp.2022.103830
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：
This paper investigates the topological interference management (TIM) problem in the face of measure-ment error in the time-varying network topology. On the one side, we consider multi-antenna users as a general application. On the other side, we consider that the observed network topology information may be incorrect due to some unknown factors. To handle the TIM problem in this scenario, a new opti-mization formulation for low-rank tensor recovery (LRTR) has been proposed as well as a new algorithm. Simulation results show that the novel low-rank tensor recovery TIM algorithm has superior performance in general networks. (c) 2022 Published by Elsevier Inc.
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页数：7
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