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

Authors

Wei Xu, The National Mobile Communications Research Lab, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, 210096, China & Purple Mountain Laboratories, Nanjing, 211111, China
Zhaohui Yang, The Department of Electronic and Electrical Engineering, University College London, London, WC1E 6BT, United Kingdom
Derrick Wing Kwan Ng, The School of Electrical Engineering and Telecommunications, The University of New South Wales, Australia
Marco Levorato, The Department of Computer Science, University of California, Irvine, 92697, CA, United States
Yonina C. Eldar, The Faculty of Math and CS, Weizmann Institute of Science, Rehovot, 7610001, Israel
Mérouane Debbah, The Technology Innovation Institute & Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

arXiv

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. Copyright © 2022, The Authors. All rights reserved.

DOI

10.48550/arXiv.2206.00422

Publication Date

6-1-2022

Keywords

Artificial intelligence (AI), beyond 5G (B5G), communication optimization, deep learning (DL), edge learning (EL), federated learning (FL), Internet-of-Everything (IoE), multi-agent reinforcement learning (MARL), Distributed, Parallel, and Cluster Computing (cs.DC), Information Theory (cs.IT), Information Theory (math.IT)

Comments

IR Deposit conditions: non-described

Preprint available on arXiv

Recommended Citation

W. Xu, Z. Yang, D.W.K. Ng, M. Levorato, Y.C. Eldar, and M. Debbah, "Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing", 2022, arXiv:2206.00422