Title: Algorithm Unrolling for Massive Connectivity in IoT Networks
Abstract: Chapter 13 Algorithm Unrolling for Massive Connectivity in IoT Networks Yinan Zou, Yinan Zou School of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSearch for more papers by this authorYong Zhou, Yong Zhou School of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSearch for more papers by this authorYuanming Shi, Yuanming Shi School of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSearch for more papers by this author Yinan Zou, Yinan Zou School of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSearch for more papers by this authorYong Zhou, Yong Zhou School of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSearch for more papers by this authorYuanming Shi, Yuanming Shi School of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSearch for more papers by this author Book Editor(s):Yuanwei Liu, Yuanwei Liu Queen Mary University of London, UK, United KingdomSearch for more papers by this authorLiang Liu, Liang Liu Hong Kong Polytechnic University, Hong Kong, Hong KongSearch for more papers by this authorZhiguo Ding, Zhiguo Ding University of Manchester, UK, United KingdomSearch for more papers by this authorXuemin Shen, Xuemin Shen University of Waterloo, Waterloo, Ontario, CanadaSearch for more papers by this author First published: 19 January 2024 https://doi.org/10.1002/9781394180523.ch13 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary In this book chapter, grant-free uplink transmission in an Internet of Things (IoT) network that comprises a base station (BS) with multiple antennas and massive single-antenna IoT devices is studied. Given the intermittent data traffic, we approach the challenge by formulating the joint activity detection and channel estimation (JADCE) problem as a group–sparse matrix recovery problem. While existing compressed sensing methods can address this formulated problem, they often grapple with the constraint of high computational complexity. Therefore, by leveraging algorithm unrolling, we propose an unrolled recurrent neural network (RNN) based on iterative shrinkage thresholding algorithm to simultaneously reduce computational complexity and improve matrix recovery performance. Recognizing that a nonconvex sparsity-induced penalty can more effectively encourage sparsity compared to the -norm, we proceed to reframe the JADCE problem as a minimax concave penalty (MCP)-regularized problem. This problem can be effectively addressed by adopting the proximal operator method, followed by proposing unrolled neural networks via parameterizing the algorithmic iterations. Theoretical analysis demonstrates the linear convergence of the proposed neural networks. Simulation results demonstrate that the neural networks we propose not only attain a faster convergence rate but also outperform the baselines in terms of recovery performance. a References Amir Beck and Marc Teboulle . A fast iterative shrinkage-thresholding algorithm for linear inverse problems . SIAM Journal on Imaging Sciences , 2 ( 1 ): 183 – 202 , 2009 . 10.1137/080716542 Web of Science®Google Scholar M Borgerding , P Schniter , and S Rangan . AMP-Inspired deep networks for sparse linear inverse problems . IEEE Transactions on Signal Processing , 65 ( 16 ): 4293 – 4308 , Aug. 2017 . 10.1109/TSP.2017.2708040 Web of Science®Google Scholar Xiaohan Chen , Jialin Liu , Zhangyang Wang , and Wotao Yin . Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds . In Proceedings Advances in Neural Information Processing Systems 31 (NeurIPS 2018) , 2018a . Google Scholar Zhilin Chen , Foad Sohrabi , and Wei Yu . Sparse activity detection for massive connectivity . IEEE Transactions on Signal Processing , 66 ( 7 ): 1890 – 1904 , 2018b . 10.1109/TSP.2018.2795540 Web of Science®Google Scholar David Chu . Polyphase codes with good periodic correlation properties (Corresp.) . IEEE Transactions on Information Theory , 18 ( 4 ): 531 – 532 , 1972 . 10.1109/TIT.1972.1054840 Web of Science®Google Scholar Ying Cui , Shuaichao Li , and Wanqing Zhang . Jointly sparse signal recovery and support recovery via deep learning with applications in MIMO-based grant-free random access . IEEE Journal on Selected Areas in Communications , 39 ( 3 ): 788 – 803 , 2020 . 10.1109/JSAC.2020.3018802 Web of Science®Google Scholar Raja Giryes , Yonina C Eldar , Alex M Bronstein , and Guillermo Sapiro . Tradeoffs between convergence speed and reconstruction accuracy in inverse problems . IEEE Transactions on Signal Processing , 66 ( 7 ): 1676 – 1690 , 2018 . 10.1109/TSP.2018.2791945 Web of Science®Google Scholar Karol Gregor and Yann LeCun . Learning fast approximations of sparse coding . In ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning , pages 399 – 406 , 2010 . Google Scholar Monowar Hasan , Ekram Hossain , and Dusit Niyato . Random access for machine-to-machine communication in LTE-advanced networks: Issues and approaches . IEEE Communications Magazine , 51 ( 6 ): 86 – 93 , 2013 . 10.1109/MCOM.2013.6525600 Web of Science®Google Scholar Qi He , Tony Q S Quek , Zhi Chen , Qi Zhang , and Shaoqian Li . Compressive channel estimation and multi-user detection in C-RAN with low-complexity methods . IEEE Transactions on Wireless Communications , 17 ( 6 ): 3931 – 3944 , 2018 . 10.1109/TWC.2018.2818125 Web of Science®Google Scholar Tao Jiang , Yuanming Shi , Jun Zhang , and Khaled B Letaief . Joint activity detection and channel estimation for IoT networks: Phase transition and computation-estimation tradeoff . IEEE Internet of Things Journal , 6 ( 4 ): 6212 – 6225 , 2018 . 10.1109/JIOT.2018.2881486 Web of Science®Google Scholar Jeremy Johnston and Xiaodong Wang . Model-based deep learning for joint activity detection and channel estimation in massive and sporadic connectivity . IEEE Transactions on Wireless Communications , 21 ( 11 ): 9806 – 9817 , 2022 . 10.1109/TWC.2022.3179600 Web of Science®Google Scholar Yoshiyuki Kabashima . A CDMA multiuser detection algorithm on the basis of belief propagation . Journal of Physics A: Mathematical and General , 36 ( 43 ): 11111 , Oct. 2003 . 10.1088/0305-4470/36/43/030 Google Scholar Malong Ke , Zhen Gao , Yongpeng Wu , Xiqi Gao , and Robert Schober . Compressive sensing-based adaptive active user detection and channel estimation: Massive access meets massive MIMO . IEEE Transactions on Signal Processing , 68 : 764 – 779 , 2020 . 10.1109/TSP.2020.2967175 Web of Science®Google Scholar Khaled B Letaief , Yuanming Shi , Jianmin Lu , and Jianhua Lu . Edge artificial intelligence for 6G: Vision, enabling technologies, and applications . IEEE Journal on Selected Areas in Communications , 40 ( 1 ): 5 – 36 , 2021 . 10.1109/JSAC.2021.3126076 Web of Science®Google Scholar Jialin Liu and Xiaohan Chen . ALISTA: Analytic weights are as good as learned weights in Lista. In Proceedings of the International Conference on Learning Representations (ICLR), 2019 . Google Scholar Liang Liu and Wei Yu . Massive connectivity with massive MIMO—Part I: Device activity detection and channel estimation . IEEE Transactions on Signal Processing , 66 ( 11 ): 2933 – 2946 , 2018 . 10.1109/TSP.2018.2818082 Web of Science®Google Scholar Liang Liu , Erik G Larsson , Wei Yu , Petar Popovski , Cedomir Stefanovic , and Elisabeth De Carvalho . Sparse signal processing for grant-free massive connectivity: A future paradigm for random access protocols in the Internet of Things . IEEE Signal Processing Magazine , 35 ( 5 ): 88 – 99 , 2018 . 10.1109/MSP.2018.2844952 Web of Science®Google Scholar Vishal Monga , Yuelong Li , and Yonina C Eldar . Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing . IEEE Signal Processing Magazine , 38 ( 2 ): 18 – 44 , 2021 . 10.1109/MSP.2020.3016905 Web of Science®Google Scholar Yurii Evgen'evich Nesterov . A method of solving a convex programming problem with convergence rate O(1/k 2 ) . In Doklady Akademii Nauk , volume 269 , pages 543 – 547 . Russian Academy of Sciences , 1983 . Google Scholar Xiaodan Shao , Xiaoming Chen , and Rundong Jia . A dimension reduction-based joint activity detection and channel estimation algorithm for massive access . IEEE Transactions on Signal Processing , 68 : 420 – 435 , 2019 . 10.1109/TSP.2019.2961299 Web of Science®Google Scholar Xiaodan Shao , Xiaoming Chen , Yiyang Qiang , Caijun Zhong , and Zhaoyang Zhang . Feature-aided adaptive-tuning deep learning for massive device detection . IEEE Journal on Selected Areas in Communications , 39 ( 7 ): 1899 – 1914 , 2021a . 10.1109/JSAC.2021.3078500 Web of Science®Google Scholar Xiaodan Shao , Xiaoming Chen , Caijun Zhong , and Zhaoyang Zhang . Exploiting simultaneous low-rank and sparsity in delay-angular domain for millimeter-wave/terahertz wideband massive access . IEEE Transactions on Wireless Communications , 21 ( 4 ): 2336 – 2351 , 2021b . 10.1109/TWC.2021.3111225 Web of Science®Google Scholar Shree Krishna Sharma and Xianbin Wang . Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions . IEEE Communications Surveys & Tutorials , 22 ( 1 ): 426 – 471 , 2019 . 10.1109/COMST.2019.2916177 Web of Science®Google Scholar Yuanming Shi , Kai Yang , Tao Jiang , Jun Zhang , and Khaled B Letaief . Communication-efficient edge AI: Algorithms and systems . IEEE Communications Surveys & Tutorials , 22 ( 4 ): 2167 – 2191 , 2020 . 10.1109/COMST.2020.3007787 Web of Science®Google Scholar Yandong Shi , Hayoung Choi , Yuanming Shi , and Yong Zhou . Algorithm unrolling for massive access via deep neural networks with theoretical guarantee . IEEE Transactions on Wireless Communications , 21 ( 2 ): 945 – 959 , 2022 . doi: 10.1109/TWC.2021.3100500. 10.1109/TWC.2021.3100500 Web of Science®Google Scholar Yuanming Shi , Yong Zhou , Dingzhu Wen , Youlong Wu , Chunxiao Jiang , and Khaled B Letaief . Task-oriented communications for 6G: Vision, principles, and technologies . IEEE Wireless Communications , 30 ( 3 ): 78 – 85 , 2023 . doi: 10.1109/MWC.002. 2200468. 10.1109/MWC.002.2200468 Web of Science®Google Scholar Zhibin Wang , Jiahang Qiu , Yong Zhou , Yuanming Shi , Liqun Fu , Wei Chen , and Khaled B Letaief . Federated learning via intelligent reflecting surface . IEEE Transactions on Wireless Communications , 21 ( 2 ): 808 – 822 , 2021 . 10.1109/TWC.2021.3099505 Web of Science®Google Scholar Zixin Wang , Yong Zhou , Yinan Zou , Qiaochu An , Yuanming Shi , and Mehdi Bennis . A graph neural network learning approach to optimize RIS-assisted federated learning . IEEE Transactions on Wireless Communications , 22 ( 9 ): 6092 – 6106 , 2023 . 10.1109/TWC.2023.3239400 Web of Science®Google Scholar Yongpeng Wu , Xiqi Gao , Shidong Zhou , Wei Yang , Yury Polyanskiy , and Giuseppe Caire . Massive access for future wireless communication systems . IEEE Wireless Communications , 27 ( 4 ): 148 – 156 , 2020 . 10.1109/MWC.001.1900494 Web of Science®Google Scholar Ming Yuan and Yi Lin . Model selection and estimation in regression with grouped variables . Journal of the Royal Statistical Society Series B (Statistical Methodology) , 68 ( 1 ): 49 – 67 , 2006 . 10.1111/j.1467-9868.2005.00532.x Web of Science®Google Scholar Weifeng Zhu , Meixia Tao , Xiaojun Yuan , and Yunfeng Guan . Deep-learned approximate message passing for asynchronous massive connectivity . IEEE Transactions on Wireless Communications , 20 ( 8 ): 5434 – 5448 , 2021 . 10.1109/TWC.2021.3067903 Web of Science®Google Scholar Justin Ziniel and Philip Schniter . Efficient high-dimensional inference in the multiple measurement vector problem . IEEE Transactions on Signal Processing , 61 ( 2 ): 340 – 354 , 2012 . 10.1109/TSP.2012.2222382 Web of Science®Google Scholar Yinan Zou , Yong Zhou , Yuanming Shi , and Xu Chen . Learning proximal operator methods for massive connectivity in IoT networks . In 2021 IEEE Global Communications Conference (GLOBECOM) , pages 1 – 6 , 2021 . doi: 10.1109/GLOBECOM46510.2021.9685447. Google Scholar Yinan Zou , Zixin Wang , Xu Chen , Haibo Zhou , and Yong Zhou . Knowledge-guided learning for transceiver design in over-the-air federated learning . IEEE Transactions on Wireless Communications , 22 ( 1 ): 270 – 285 , 2022 . 10.1109/TWC.2022.3192550 Web of Science®Google Scholar Next Generation Multiple Access ReferencesRelatedInformation
Publication Year: 2024
Publication Date: 2024-01-19
Language: en
Type: other
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot