Call for Papers

Topic Summary

The extreme range of 5G requirements for user experience, efficiency, performance and complex network environments, makes the design and optimization of 5G networks very challenging. The future 5G network will require robust intelligent algorithms to adapt network protocols and resource management for different services in different scenarios. Therefore, Machine Learning (ML) and Artificial Intelligence (AI) approaches, well known in computer science disciplines as powerful tools to solve complex problems, are beginning to be applied also in wireless communications. These ML/AI approaches have been first widely used to address problems in the upper layers of wireless communication systems, such as the deployment of cognitive radio and communication networks, yielding significant performance improvements over communication systems designed with traditional methods. Fueled by such successes, and stimulated by the challenges of future communications, such as the demand for high speed connections in complex scenarios with unknown channel models, ML/AI technologies have now been considered also at the physical-layer of wireless communications, challenging conventional communication theories. The recent advances in deep learning, convolutional neural networks and reinforcement learning, indeed, hold significant promise for solving very complex problems considered intractable until now. Also, with the development of multimedia communications and Internet of Things, physical layer security is now emerging as a promising means of defense to realize wireless secrecy in communications. In physical layer security, the core idea is to exploit the characteristics of wireless channels such as fading or noise to design efficient secure transmission strategies, such that the message from the source to the intended receiver are kept confidential from both passive and active eavesdroppers. By applying ML/AL technology, the physical layer security paradigm can be further improved compared with conventional security technologies.

Therefore, the application of ML and AI for physical layer design and optimization should be deeply investigated to make future communications more intelligent, agile, and robust. This special issue will bring together academic and industrial researchers to identify and discuss technical challenges and recent results related to application of ML and AI for the physical layer. Topics of interest include but are not limited to the following:

• ML/AI based 5G physical layer technologies
• ML/AI based beamforming in massive MIMO system, mmWave system
• ML/AI based non-orthogonal multiple access (NOMA) techniques
• ML/AI based modulation and coding
• ML/AI based channel modeling
• ML/AI based energy-efficient network operations
• ML/AI based ultra-dense cell communication
• ML/AI based unmanned aerial vehicle communication
• ML/AI based physical-layer methods for secrecy and privacy for 5G and the IoT
• ML/AI based physical-layer security in co-located and distributed massive MIMO
• ML/AI based secure transmission using physical layer characteristics at mmWave and THz frequencies
• ML/AI based physical layer authentication and location verification

Submission Guidelines

Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Cognitive Communications and Networking guidelines. Please submit your papers through the online system and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.

Important Dates

Submission Deadline: 1 May 2020
First Reviews Complete: 1 July 2020
Revision Due: 15 August 2020
Final Review Decision: 15 October 2020
Final to Publisher: 1 November 2020
Publication: First Quarter 2021

Guest Editors