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Publication Date

Manuscript Submission Deadline


Call for Papers

The global demand for data traffic has experienced explosive growth over the past years. In the era of the new generation of communication systems, data traffic is expected to continuously straining the capacity of future communication networks. Along with the remarkable growth in data traffic, new applications of communications, such as wearable devices, autonomous systems, drones, and the Internet of Things (IoT), continue to emerge and generate even more data traffic with vastly different performance requirements. This growth in the application domain brings forward an inevitable need for more intelligent processing, operation, and optimization of tomorrow’s communication networks.

To realize this vision of intelligent processing and operation, there is a need to integrate machine learning, known to be the vessel that carries artificial intelligence, into the design, planning, and optimization of future communication networks. Particularly, the emerging framework of deep learning can be a key enabler for intelligent processing in a broad range of scenarios. Modern machine learning techniques provide ample opportunities to enable intelligent communication designs while addressing various problems ranging from signal detection, classification, and sparse signal recovery to channel modeling, network optimization, resource management, routing, transport protocol design, and application/user behavior analysis.

Beyond intelligent network management, machine learning will allow future communication networks and their applications, e.g., IoT, to exploit big data analytics so as to enhance situational awareness and overall network operation. Particularly, the massive amounts of data generated from multiple sources that range from network measurements to IoT sensor readings as well as drones and surveillance images can be used to show the comprehensive operational view of the massive number of devices within the network. Additionally, this comprehensive view can be exploited to detect anomaly events in communication networks.

This JSAC Series will focus on machine learning solutions to problems in communication networks, across various layers and within a broad range of applications. The topics of interest include, but are not limited to, machine learning, especially deep learning, for signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction, software defined networking, congestion control, communication network optimization, security and anomaly detection. The objective of this series is to bring together the state-of-art research results and industrial applications of machine learning for intelligent communications. Original contributions previously unpublished and not currently under review by another journal are solicited in relevant areas, including (but not limited to) the following:

  • Machine/deep learning for signal detection, channel modeling, estimation, interference mitigation, and decoding.
  • Resource and network optimization using machine learning techniques.
  • Distributed learning algorithms and implementations over realistic communication networks.
  • Machine learning techniques for application/user behavior prediction and user experience modeling and optimization.
  • Machine learning techniques for anomaly detection in communication networks.
  • Machine learning for emerging communication systems and applications, such as drone systems, IoT, edge computing, caching, smart cities, and vehicular networks.
  • Machine learning for transport-layer congestion control.
  • Machine learning for integrated radio frequency/non-radio frequency communication systems.
  • Machine learning techniques for information-centric networks and data mining.
  • Machine learning for network slicing, network virtualization, and software defined networking.
  • Performance analysis and evaluation of machine learning techniques in wired/wireless communication systems.
  • Scalability and complexity of machine learning in networks.
  • Techniques for efficient hardware implementation of neural networks in communications.
  • Synergies between distributed/federated learning and communications.
  • Secure machine learning over communication networks.

Submission Guidelines

Prospective authors should submit their manuscripts following the IEEE JSAC guidelines. Authors should submit a PDF version of their complete manuscript to EDAS according to the following schedule:

Important Dates

Inauguration Issue

Manuscript Submission: 15 July 2020 (Deadline Extended)
First Notification: 25 August 2020
Revised Paper Due: 20 September 2020
Acceptance Notification: 20 October 2020
Final Manuscript Due: 1 November 2020
Publication Date: January 2021

Next Issue

Manuscript Submission: 1 January 2021
First Notification: 2 February 2021
Revised Paper Due: 20 March 2021
Acceptance Notification: 20 April 2021
Final Manuscript Due: 1 May 2021
Publication Date: July 2021


Geoffrey Li
Georgia Tech

Associate Editors-in-Chief

Walid Saad
Virginia Tech

Ayfer Ozgur
Stanford University

Peter Kairouz
Google, Inc.

Founding Editorial Board

Jakob Hoydis
Nokia Bell Labs

Elisabeth de Carvalho
Aalborg University

Alexios Balatsoukas-Stimming
Eindhoven University of Technology

Zhijin Qin
Queen Mary University of London

Xiangwei Zhou
Louisiana State University

Tommy Svensson
Chalmers University of Technology

Khaled Letaief
Hong Kong University of Science and Tech