Call for Papers

Programmable virtualized networks decouple data and control planes and have the power of supporting the demands of diverse and varied use cases, allowing the services to scale and adapt dynamically. The diverse demands of Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-latency Communications (uRLLC), and massive Machine Type Communications (mMTC) users inevitably increase the network complexity, making programmability alone not sufficient. To fulfill these demands, next generation mobile networks need to respond with the capacity of supporting a greater number of heterogeneous devices, the availability of more spectrum and the introduction of complex protocol stacks, making automated and intelligent tools for management, reconfiguration, adaptation, and coordination of network resources an impelling necessity.

Disaggregated RAN and core are supported by close control loops with intelligence, responding to the dynamic demands for such diverse and heterogenous quality of service (QoS), and to the changes in user needs, environmental conditions and business goals.  As network complexity grows at all levels and new challenges arise, network intelligence and data-driven approaches become a necessity. AI/ML technologies are expected to play a key role in this evolution, and we expect that will shape how the next generation mobile networks will operate. Artificial intelligence (AI), or more specifically machine learning (ML) algorithms, stand as promising tools to intelligently manage the networks such that network efficiency reliability and robustness goals are achieved, quality of service demands is satisfied, network and computational resources are used most efficiently, and performance targets are achieved in a self-optimized manner. Indeed, AI will represent a key enabler for Open Programmable Virtualized Networks in 6G and will be fundamental to foster network automation and dynamic reconfiguration of the network based on the contingent operating context and QoS applications’ requirements.

This Special Issue will explore the role of AI in 6G and its impact on the design of open programmable networks in the next-generation wireless networks.​ List of potential topics to be covered by the special issue are:

• AI for mobile network automation beyond 5G
• Distributed AI for closed-loop network automation
• AI at the Edge
• Cognitive networking at the network edge
• Intelligent programmability of disaggregated RAN
• Network visibility in intelligent programmable virtualized networks
• AI-assisted network slicing
• AI for network slice assurance
• Explainable AI for networks
• Testbed infrastructures for testing AI solutions for 6G

Submission Guidelines

Prospective authors should prepare their submissions in accordance with the rules specified in the Information for Authors of the IEEE Wireless Communications guidelines. Authors should submit a PDF version of their complete manuscript to Manuscript Central. The timetable is as follows:

Important Dates

Submission Deadline: 1 February 2022 15 February 2022 (Extended Deadline)
Initial Decision Date: 1 April 2022
Revised Manuscript Due: 1 May 2022
Final Decision Date: 1 July 2022
Final Manuscript Due: 15 August 2022
Publication Date: October 2022

Guest Editors

Fabrizio Granelli
University of Trento, Italy

Cristina E. Costa
FBK, Italy

Melike Erol-Kantarci
University of Ottawa, Canada

Jun Zheng
Southeast University, China