Sparse signal processing for massive connectivity via mixed-integer programming

doi:10.1109/ICCC52777.2021.9580226

	Sparse signal processing for massive connectivity via mixed-integer programming
	Shuang Liang; Yuanming Shi; Yong Zhou
	2021-07-28
会议录名称	2021 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC 2021
ISSN	2377-8644
页码	272-276
发表状态	已发表
DOI	10.1109/ICCC52777.2021.9580226
摘要	Massive connectivity is a critical challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multi-antenna base station (BS) and a large number of single-antenna IoT devices. Due to the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. Although many algorithms have been proposed to solve the JADCE problem, most of them are developed based on compressive sensing technique, yielding suboptimal solutions. In this paper, we first develop an efficient weighted $l_{1}$ -norm minimization algorithm to better approximate the group sparsity than the existing mixed $l_{1}/l_{2}$.. norm minimization. Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted l1 -norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution. To this end, we further reformulate the JADCE problem as a mixed integer programming (MIP) problem, which can be solved by using the branch-and-bound method. As a result, we are able to obtain an optimal solution of the JADCE problem, which can be adopted as an upper bound to evaluate the effectiveness of the existing algorithms. Moreover, we also derive the minimum pilot sequence length required to fully recover the estimated matrix in the noiseless scenario. Simulation results show the performance gains of the proposed optimal algorithm over the proposed weighted $l_{1}$-norm algorithm and the conventional mixed $l_{1}/l_{2}$ norm algorithm. Results also show that the proposed algorithms require a short pilot sequence than the conventional algorithm to achieve the same estimation performance. © 2021 IEEE.
关键词	Antennas Branch and bound method Channel estimation Integer programming Internet of things Matrix algebra Mean square error Relaxation processes Signal processing Activity detection Estimation problem Group sparse Joint activity Joint activity detection and channel estimation Massive connectivity Matrix estimation Mixed Integer Programming Optimal solutions Sparse matrices
会议名称	2021 IEEE/CIC International Conference on Communications in China, ICCC 2021
会议地点	Xiamen, China
会议日期	July 28, 2021 - July 30, 2021
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20214711202548
EI主题词	Optimal systems
EI分类号	716.1 Information Theory and Signal Processing ; 722.3 Data Communication, Equipment and Techniques ; 723 Computer Software, Data Handling and Applications ; 921.1 Algebra ; 921.5 Optimization Techniques ; 922.2 Mathematical Statistics ; 931.1 Mechanics ; 961 Systems Science
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/133519
专题	信息科学与技术学院信息科学与技术学院_PI研究组_石远明组信息科学与技术学院_PI研究组_周勇组信息科学与技术学院_硕士生
作者单位	School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Shuang Liang,Yuanming Shi,Yong Zhou. Sparse signal processing for massive connectivity via mixed-integer programming[C]:Institute of Electrical and Electronics Engineers Inc.,2021:272-276.