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Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach | |
2022-08-01 | |
发表期刊 | IEEE INTERNET OF THINGS JOURNAL |
ISSN | 2327-4662 |
EISSN | 2327-4662 |
卷号 | 9期号:15 |
发表状态 | 已发表 |
DOI | 10.1109/JIOT.2022.3142185 |
摘要 | This paper investigates the grant-free random access mechanism for massive Internet of Things (IoT) devices. By embedding the data symbols in the signature sequences, joint device activity detection and data decoding can be achieved, which, however, significantly increases the computational complexity. Coordinate descent algorithms that enjoy a low per-iteration complexity have been employed to solve this detection problem, but previous works typically employ a random coordinate selection policy which leads to slow convergence. In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which achieves a delicate trade-off between exploration and exploitation in coordinate selection. Specifically, we first propose a bandit based strategy, i.e., Bernoulli sampling, to speed up the convergence rate of coordinate descent, by learning which coordinates will result in more aggressive descent of the nonconvex objective function. To further improve the convergence rate, an inner multi-armed bandit problem is established to learn the exploration policy of Bernoulli sampling. Both convergence rate analysis and simulation results are provided to show that the proposed bandit based algorithms enjoy faster convergence rates with a lower time complexity compared with the state-of-the-art algorithm. Furthermore, our proposed algorithms are generally applicable to different scenarios, e.g., massive random access with low-precision analog-to-digital converters (ADCs). IEEE |
关键词 | Analog to digital conversion Decoding Economic and social effects Iterative methods Linear programming Maximum likelihood Convergence Coordinate descent Linear-programming Massive connectivity Maximum-likelihood decoding Multiarmed bandits (MABs) Partial transmit sequence Payload Thompson sampling. Thompson samplings |
URL | 查看原文 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62072269] ; General Research Fund through the Hong Kong Research Grants Council[15207220] |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS记录号 | WOS:000831217100064 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20220411531039 |
EI主题词 | Internet of things |
EI分类号 | 722.3 Data Communication, Equipment and Techniques ; 723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 921.6 Numerical Methods ; 922.1 Probability Theory ; 971 Social Sciences |
原始文献类型 | Article in Press |
来源库 | IEEE |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/154119 |
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 |
作者单位 | 1.Department of Electrical and Computer Engineering, University of California at Los Angeles, Los Angeles, CA, USA 2.Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong 3.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 4.Institute for Network Sciences and Cyberspace, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China |
推荐引用方式 GB/T 7714 | Jialin Dong,Jun Zhang,Yuanming Shi,et al. Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach[J]. IEEE INTERNET OF THINGS JOURNAL,2022,9(15). |
APA | Jialin Dong,Jun Zhang,Yuanming Shi,&Jessie Hui Wang.(2022).Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach.IEEE INTERNET OF THINGS JOURNAL,9(15). |
MLA | Jialin Dong,et al."Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach".IEEE INTERNET OF THINGS JOURNAL 9.15(2022). |
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