Guest Editorial Distributed Signal Processing for Edge Learning in B5G IoT Networks

Wei Xu; Derrick Wing Kwan Ng; Marco Levorato; Yonina C. Eldar; Merouane Debbah

doi:10.1109/JSTSP.2022.3227720

Guest Editorial Distributed Signal Processing for Edge Learning in B5G IoT Networks

Wei Xu, Derrick Wing Kwan Ng, Marco Levorato, Yonina C. Eldar, Merouane Debbah

Department of Computer Science and Applied Mathematics

Weizmann Institute of Science

Research output: Contribution to journal › Editorial

Abstract

The papers in this special section focus on distributed signal processing for edge learning (EL). EL is a new and promising technology for implementing artificial intelligence (AI) algorithms at edge devices over wireless networks. With the explosive growth in global data traffic, the number of edge devices such as mobile edge computing (MEC), satellite networks, and the Internet-ofthings (IoT) devices increase rapidly. Machine learning (ML) techniques, including deep learning (DL), federated learning (FL), and reinforcement learning (RL), are effective approaches to improve the performance and efficiency of edge networks.

Original language	English
Pages (from-to)	3-8
Number of pages	6
Journal	IEEE Journal on Selected Topics in Signal Processing
Volume	17
Issue number	1
DOIs	https://doi.org/10.1109/JSTSP.2022.3227720
State	Published - 1 Jan 2023

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

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10.1109/JSTSP.2022.3227720

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title = "Guest Editorial Distributed Signal Processing for Edge Learning in B5G IoT Networks",

abstract = "The papers in this special section focus on distributed signal processing for edge learning (EL). EL is a new and promising technology for implementing artificial intelligence (AI) algorithms at edge devices over wireless networks. With the explosive growth in global data traffic, the number of edge devices such as mobile edge computing (MEC), satellite networks, and the Internet-ofthings (IoT) devices increase rapidly. Machine learning (ML) techniques, including deep learning (DL), federated learning (FL), and reinforcement learning (RL), are effective approaches to improve the performance and efficiency of edge networks.",

author = "Wei Xu and Ng, \{Derrick Wing Kwan\} and Marco Levorato and Eldar, \{Yonina C.\} and Merouane Debbah",

note = "The guest editors would like to express their sincere gratitude to all the authors for submitting their valuable contributions to this SI. The guest editors are also grateful to all the reviewers for their constructive comments and suggestions. The guest editors are also indebted to the Executive Editor Mikaela Langdon for supporting this SI",

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doi = "10.1109/JSTSP.2022.3227720",

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AU - Levorato, Marco

AU - Eldar, Yonina C.

AU - Debbah, Merouane

N1 - The guest editors would like to express their sincere gratitude to all the authors for submitting their valuable contributions to this SI. The guest editors are also grateful to all the reviewers for their constructive comments and suggestions. The guest editors are also indebted to the Executive Editor Mikaela Langdon for supporting this SI

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N2 - The papers in this special section focus on distributed signal processing for edge learning (EL). EL is a new and promising technology for implementing artificial intelligence (AI) algorithms at edge devices over wireless networks. With the explosive growth in global data traffic, the number of edge devices such as mobile edge computing (MEC), satellite networks, and the Internet-ofthings (IoT) devices increase rapidly. Machine learning (ML) techniques, including deep learning (DL), federated learning (FL), and reinforcement learning (RL), are effective approaches to improve the performance and efficiency of edge networks.

AB - The papers in this special section focus on distributed signal processing for edge learning (EL). EL is a new and promising technology for implementing artificial intelligence (AI) algorithms at edge devices over wireless networks. With the explosive growth in global data traffic, the number of edge devices such as mobile edge computing (MEC), satellite networks, and the Internet-ofthings (IoT) devices increase rapidly. Machine learning (ML) techniques, including deep learning (DL), federated learning (FL), and reinforcement learning (RL), are effective approaches to improve the performance and efficiency of edge networks.

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Guest Editorial Distributed Signal Processing for Edge Learning in B5G IoT Networks

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