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Cited by 660

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Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing

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Xu, Wei, Yang, Zhaohui, Ng, Derrick Wing Kwan, Levorato, Marco, Eldar, Yonina C., Debbah, Merouane·2023·IEEE Journal of Selected Topics in Signal Processing
DOI 10.1109/jstsp.2023.3239189
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Abstract

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In this paper, we provide an overview on practical distributed EL techniques and their interplay with advanced communication optimization designs. In particular, typical performance metrics for dual-functional learning and communication networks are discussed. Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of “communications for learning” and “learning for communications.” The application of EL techniques within a variety of future communication systems are also envisioned for beyond 5G (B5G) wireless networks. For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem. In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks. We also present technical challenges as well as emerging application opportunities in this field, with the aim of inspiring future research and promoting widespread developments of EL in B5G.

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Metadata

Authors
Xu, W., Yang, Z., Ng, D. W. K., Levorato, M., Eldar, Y. C., & Debbah, M.
Venue
IEEE Journal of Selected Topics in Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-01
Volume
17
Issue
1
Pages
9-39
ISSN
  • 1932-4553 (ISSN-L)
  • 1941-0484
DOI
10.1109/jstsp.2023.3239189
OpenAlex
W4317794926
Cited by
660
References
51
FWCI
72.09
Type
journal article
Language
en
Open access
green
Licence
Funders
  • National Key Research and Development Program of China (2020YFB1806608)
  • National Natural Science Foundation of China (62022026, 62211530108)
  • Australian Research Council's Discovery Project (DP210102169, DP230100603)

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The paper
Xu, W., Yang, Z., Ng, D. W. K., Levorato, M., Eldar, Y. C., & Debbah, M. (2023). Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/jstsp.2023.3239189

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