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Publications

Publication Date

Manuscript Submission Deadline

Special Feature

Call for Papers

Beyond the fifth-generation (B5G) networks, or so-called “6G”, is emerging to support a massive number of users’ connectivity and multi-gigabits transmission rate. B5G networks should be intelligent enough to adapt to very dynamic topologies, intensive computation and storage applications, and diverse QoS requirements for ultra-high efficiency and resiliency purposes.

It is envisioned that blockchain and AI will be two very important technologies for the successful development of the future B5G or 6G networks. B5G networks encounter fundamental challenges from resource-constrained devices to radio resource management and the underlying heterogeneous networking and computing infrastructures. To tackle the challenges, joining blockchain and AI into B5G networks may achieve an elegant breakthrough in terms of seamless interoperability, low cost, high security and privacy-preservation, and increased efficiency. Since blockchain technologies employ a distributed ledger to offer secure services without a third party, blockchain has the high potential to offer an underlying secure and transparent networking foundation for B5G. The FCC (Federal Communications Commission, U.S.) also believes that blockchain will be a key technology for dynamic spectrum sharing in 6G. On the other hand, AI approaches, e.g., reinforcement learning and deep learning, can be incorporated into B5G networks to process data and manage radio and computation resources in an intelligent and efficient way. Further, blockchain and AI are not independent from each other. AI approaches are able to determine the optimal parameters for blockchain, which may dramatically improve the computation, communications and storage performance of B5G networks.

Until now, limited research efforts have been made and few papers have been published on blockchain and AI for B5G networks. The scope of this Feature Topic (FT) is to present and highlight the advances and the latest implementations and applications in the field of blockchain and AI for B5G networks such that the theoretical and practical frontiers can be moved forward for a deeper understanding from both academic and industrial viewpoints. We particularly have interest in the submissions on joining blockchain and AI together to dramatically improve the performance of B5G networks. Possible topics include but are not limited to:

  • Blockchain and AI oriented B5G networks infrastructure
  • High throughput blockchain architecture with AI for B5G networks
  • Low latency blockchain architecture with AI for B5G networks
  • New consensus protocols with AI approaches for B5G networks
  • Scalable services for blockchain and AI enabled B5G networks
  • Security and privacy in blockchain and AI enabled B5G networks
  • Mobile edge computing for Blockchain and AI empowered B5G networks
  • Blockchain and AI for dynamic spectrum sharing in B5G networks
  • Blockchain based secure infrastructure management for B5G networks
  • AI empowered resource management for B5G networks
  • Intelligent communication technologies for B5G networks
  • Machine/deep learning based optimization methods for B5G networks
  • New opportunities, challenges, case studies, and applications for blockchain and AI in B5G networks

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 “November 2020: Blockchain and AI for Beyond 5G Networks” from the drop-down menu of Topic/Series titles.

Important Dates

Manuscript Submission Deadline: 30 November 2019
Initial Decision Notification: 15 March 2020
Final Decision Notification: 31 July 2020
Publication Date: November 2020

Guest Editors

Yan Zhang
University of Oslo, Norway

Kun Wang
University of California, Los Angeles, USA

Hassnaa Moustafa
Intel, USA

Stephen Wang
Huawei Technologies Research & Development, UK

Ke Zhang
University of Electronic Science and Technology of China