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Cited by 60Sparse and Compressive Sensing TechniquesIoT Networks and ProtocolsDistributed Sensor Networks and Detection Algorithms

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

Open access
Jiang, Tao, Shi, Yuanming, Zhang, Jun, Letaief, Khaled B.·2019·IEEE Internet of Things Journal
DOI 10.1109/jiot.2018.2881486
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Abstract

Massive device connectivity is a crucial communication challenge for Internet of Things (IoT) networks, which consist of a large number of devices with sporadic traffic. In each coherence block, the serving base station needs to identify the active devices and estimate their channel state information for effective communication. By exploiting the sparsity pattern of data transmission, we develop a structured group sparsity estimation method to simultaneously detect the active devices and estimate the corresponding channels. This method significantly reduces the signature sequence length while supporting massive IoT access. To determine the optimal signature sequence length, we study the phase transition behavior of the group sparsity estimation problem. Specifically, user activity can be successfully estimated with a high probability when the signature sequence length exceeds a threshold; otherwise, it fails with a high probability. The location and width of the phase transition region are characterized via the theory of conic integral geometry. We further develop a smoothing method to solve the high-dimensional structured estimation problem with a given limited time budget. This is achieved by sharply characterizing the convergence rate in terms of the smoothing parameter, signature sequence length and estimation accuracy, yielding a tradeoff between the estimation accuracy and computational cost. Numerical results are provided to illustrate the accuracy of our theoretical results and the benefits of smoothing techniques.

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  • Sparse signal processing provides a promising modeling framework to simultaneously detect the active devices and estimate their channels, supported by various efficient algorithms including the approximate message passing algorithm and DNN algorithm unrolling approach.

    “Sparse signal processing provides a promising modeling framework to simultaneously detect the active devices and estimate their channels [75], [76], which is supported by various efficient algorithms, including the approximate message passing algorithm [184], [185] and DNN algorithm unrolling approach [97], [186].”

    From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [76]

  • Group sparsity can represent combinatorial variables for edge device selection in FL, edge device activity detection, and inference task selection.

    “For instance, the group sparsity can represent the combinatorial variables for edge devices selection in FL [31], edge devices activity detection [76], and inference tasks selection [53].”

    From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [76]

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Research works printed but not linked: 62

  1. pilanci, 2016, Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares, J Mach Learn Res, 17, 1842
  2. 10.1109/TIT.2015.2450722 · DOI
  3. 10.1007/978-3-540-78859-1 · DOI
  4. 10.1007/s10208-015-9278-4 · DOI
  5. 10.1093/imaiai/iau005 · DOI
  6. 10.1007/s12532-011-0029-5 · DOI
  7. 10.1137/120896219 · DOI
  8. 10.1109/TSP.2015.2443731 · DOI · Paper page
  9. 10.1561/2200000016 · DOI · Paper page
  10. rockafellar, 2015, Convex Analysis
  11. 10.1007/s10107-012-0629-5 · DOI
  12. 10.1137/S1052623403427823 · DOI
  13. 10.1007/s10107-008-0261-6 · DOI
  14. 10.1007/978-0-8176-4948-7 · DOI
  15. nemirovskii, 1983, Problem Complexity and Method Efficiency in Optimization
  16. 10.1109/ICC.2017.7996388 · DOI
  17. 10.1007/s10208-012-9135-7 · DOI
  18. 10.1109/MCOM.2017.1500269CM · DOI
  19. 10.1109/COMST.2015.2444095 · DOI · Paper page
  20. 10.1109/TCOMM.2011.121410.090570 · DOI
  21. 10.1109/TCOMM.2015.2424414 · DOI
  22. alsamhi, 2018, Predictive Estimation of the Optimal Signal Strength From Unmanned Aerial Vehicle Over Internet of Things Using ANN
  23. 10.1109/TSP.2018.2818082 · DOI · Paper page
  24. 10.1109/JSAC.2017.2726279 · DOI
  25. 10.1007/s11277-016-3514-3 · DOI
  26. 10.1109/TSP.2018.2818070 · DOI
  27. alsamhi, 2018, Greening Internet of Things for Smart Everythings With a Green-Environment Life A Survey and Future Prospects
  28. 10.1109/TII.2018.2808190 · DOI
  29. 10.1109/TCOMM.2017.2711025 · DOI
  30. 10.1098/rsta.2009.0152 · DOI
  31. 10.1109/LSP.2011.2177496 · DOI
  32. grant, 2014, CVX Matlab Software for Disciplined Convex Programming Version 2 1
  33. 10.1109/ACCESS.2018.2791580 · DOI
  34. 10.1109/TIT.2005.862083 · DOI · Paper page
  35. 10.1109/TIT.2006.871582 · DOI · Paper page
  36. 10.1146/annurev-statistics-022513-115643 · DOI
  37. 10.1109/TSP.2018.2795540 · DOI
  38. 10.1109/COMST.2017.2664421 · DOI
  39. 10.1109/TIT.2017.2773497 · DOI
  40. 10.1109/TWC.2016.2535310 · DOI · Paper page
  41. 10.1109/TWC.2018.2797969 · DOI · Paper page
  42. 10.1109/MCOM.2013.6525600 · DOI
  43. 10.1109/TWC.2017.2660489 · DOI
  44. 10.1109/MCOM.2018.1700472 · DOI · Paper page
  45. al-turjman, 0, Small cells in the forth coming 5G/IoT: Traffic modelling and deployment overview, IEEE Commun Surveys Tuts
  46. 10.1109/TIT.2017.2668391 · DOI
  47. 10.1109/MCOM.2017.1600555CM · DOI
  48. 10.1109/MSP.2018.2844952 · DOI · Paper page
  49. 10.1109/JIOT.2018.2825098 · DOI
  50. 10.1109/JIOT.2015.2394438 · DOI
  51. 10.1109/ACCESS.2015.2407194 · DOI
  52. ghosh, 2010, Fundamentals of LTE
  53. 10.1109/JIOT.2018.2842150 · DOI
  54. 10.1109/MCOM.2015.7263349 · DOI · Paper page
  55. 10.1016/j.comcom.2018.02.012 · DOI
  56. 10.1016/j.pmcj.2017.05.001 · DOI
  57. 10.1007/s11277-015-2333-2 · DOI
  58. 10.1109/COMST.2017.2661201 · DOI
  59. 10.1073/pnas.1302293110 · DOI
  60. 10.1109/TSP.2018.2791945 · DOI
  61. 10.1109/JSTSP.2015.2400412 · DOI
  62. 10.1137/120863290 · DOI

Cited by

Sorted by citations

  • Federated Learning via Over-the-Air Computation

    Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084

    “This sporadic property of IoT data traffic can be exploited to support massive device connectivity via jointly detecting active devices and estimating channel coefficients [54].”
    BackgroundSelf-citation

  • Edge artificial intelligence for 6g: vision, enabling technologies, and applications

    Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829

    “…power and receive beamforming [31], [48], [178], passive beamforming at RIS [48], [178], device selection [31], [242] and activity detection [76], local updates computation [92], and global aggregation frequency control [130], thereby reducing the optimality gap and energy consumption in…”
    BackgroundMethodSelf-citation

Metadata

Authors
Jiang, T., Shi, Y., Zhang, J., & Letaief, K. B.
Affiliations
  • ShanghaiTech University (CN)
  • Hong Kong University of Science and Technology (HK)
ORCID
Venue
IEEE Internet of Things Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-08
Volume
6
Issue
4
Pages
6212-6225
ISSN
  • 2327-4662 (ISSN-L)
  • 2372-2541
DOI
10.1109/jiot.2018.2881486
OpenAlex
W2893278801
Cited by
60
References
2
Citations by year
2025: 4 · 2024: 7 · 2023: 2 · 2022: 8 · 2021: 13 · 2020: 20 · 2019: 5
FWCI
7.18
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Internet of Things Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (61601290)
  • Shanghai Sailing Program (16YF1407700)
  • Hong Kong Research Grant Council (16211815)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
Keywords
Internet of Things, massive device connectivity, phase transitions, smoothing methods, sparse channel estimation, convergence rate

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