Call for Papers

To satisfy the explosively increasing demands of high-speed data applications and access requirements from a massive number of Internet-of-thing (IoT) devices, a paradigm of fog computing-based radio access networks (F-RANs) has emerged as a promising evolution path for the fifth generation (5G) radio access networks. By taking full advantages of distributed caching and centralized processing, F-RANs provide great flexibility to satisfy quality-of-service requirements of various 5G services. Unfortunately, it is quite challenging for F-RAN to achieve ultra-low-latency of less than 1.0 millisecond required by self-driving based Internet of vehicles, and 99.999% reliability, and over 10⁶ connections/km² density as required by intelligent manufacturing. To tackle these challenges, artificial intelligence (AI) approaches start to emerge in F-RANs as a promising candidate. AI-driven F-RANs can potentially lead to efficient, rapid, trustworthy management operations, which has been exemplified by recent initiatives to set-up network automation platforms.

The combination of AI with F-RANs has drawn particular attention to inter-disciplinary research interest from both wireless communication and AI communities. The aim of this Special Issue (SI) is to bring academic researchers and industry developers together for sharing the recent advances and future trends of AI-driven F-RANs. Topics of interest include, but are not limited to the following:

• Architecture design for AI-driven F-RANs in 5G systems
• Recent industry development for AI-driven F-RANs
• Advanced fronthaul/backhaul and edge cache mechanisms for AI-driven F-RANs
• Fronthaul/backhaul modeling and improvement in AI-driven F-RANs
• AR/VR and other smart IoT services in AI-driven F-RANs
• Intelligent industry services design for AI-driven F-RANs
• Information-theoretic results and impacts on AI-driven F-RANs
• Advanced communication and edge caching technologies for AI-driven F-RANs
• Resource management and cross-layer design in AI-driven F-RANs
• AI-driven resource scheduling and management for F-RANs
• Energy or cost-efficient design for AI-driven F-RANs
• Cognitive radio design and networking in AI-driven F-RANs
• Radio resource virtualizations and interference control in AI-driven F-RANs
• Joint radio, cache, and computing resource allocation in AI-driven F-RANs
• Power control, energy saving and harvesting in AI-driven F-RANs
• Mobility estimation/enhancement/modeling in AI-driven F-RANs
• Computing, communication, caching, and control (4C) in AI-driven F-RANs
• Self-organizing AI-driven F-RANs
• Network security for AI-driven F-RANs
• Advanced simulation tools for AI-driven F-RANs
• Hardware testbed or field trial for AI-driven F-RANs
• Measurement results and big data analysis for AI-driven F-RANs
• Standard definition and implementation for AI-driven F-RANs

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Paper Submission Guidelines.

All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select the “April 2020: Artificial Intelligence-Driven Fog Radio Access Networks (F-RANs): Recent Advances and Future Trends” topic from the drop-down menu of Topic/Series titles.

Important Dates

Manuscript Submission Deadline: 15 August 2019
Initial Decision: 1 October 2019
Revised Manuscript Due: 1 November 2019
Final Decision: 1 December 2019
Final Manuscript Due: 15 January 2020
Publication Date: April 2020

Guest Editors

Mugen Peng
Beijing University of Posts & Telecommunications, China

Tony Q. S. Quek
Singapore University of Technology and Design, Singapore

Guoqiang Mao
University of Technology Sydney, Australia

Zhiguo Ding
The University of Manchester, UK

Chonggang Wang
InterDigital Communications, USA