Call for Papers -- Feature Topic, Vol. 17, No. 5, 2020
AI-Empowered Millimeter Wave Communication and Networking
With the exponential increase of
users’ demands for higher data rate, high-frequency mmWave communication and networking become one of the fundamental technologies for 5G and beyond communication systems, which still face many practical challenges, including high mobility, limited coverage, non-stationary environments, etc. To this end, innovative mmWave communication and networking technologies are called for in this feature topic. Recently, the emerging artificial intelligence (AI)-empowered mmWave communication and networking have shown great potentials to address these practical challenges, where collaborative data-driven and model-driven approaches are promising solutions towards future wireless communications. Note that when AI meets mmWave technologies, the corresponding signal processing, beam alignment/tracking, resource allocation and networking require innovative ideas, designs and implementations.
The main goal of this feature topic is to explore promising theories and technologies for AI-empowered mmWave communication and networking. Therefore, it is urgent to call for original contributions of latest progresses, where the extended versions of papers published in early conferences, symposiums and workshops are also welcomed for further consideration.
Submission Deadline: Junuary 31, 2020
Acceptance Notification (1st round): February 15, 2020
Minor Revision Due: March 1, 2020
Final Decision Due: March 15, 2020
Final Manuscript Due: March 30, 2020
Publication Date: May 15, 2020
The Chinese University of Hong Kong, Shenzhen, China
Wenjun Xu, Beijing University of Posts and Telecommunications, China
Yongming Huang, Southeast University, China
Chuan Huang, University of Electronic Science and Technology of China, China
Chau Yuen, Singapore University of Technology and Design, Singapore
Topics include (but not limited to):
l Prediction based beam alignment and tracking algorithms for mmWave communications
l Machine learning (ML)-based signal processing for mmWave communications
l Inference-based robust transmission design and analysis for mobile mmWave communications
l ML-based resource allocation and interference management for mmWave networks
l Context-aware mmWave network design and performance analysis
l Collaborative data-driven and model-driven protocol and mechanism for mmWave networks
l The application of emerging AI/ML methods, e.g., reinforcement learning, representation learning, transfer learning, knowledge-based methods, etc., in mmWave communication and networking
This feature topic “AI-Empowered Millimeter Wave Communication and Networking” invites submissions of original, previously unpublished technical papers and visionary articles exploring the architecture, technologies, and applications in AI-empowered mmWave systems/networks. All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical merits. Potential topics of interest include, but not limited to areas listed above.
Each submission must be accompanied by the following information:
l an abstract of no more than 150 words
l 3-8 keywords
l original photographs with high-resolution (300 dpi or greater); eps. ortif. format is preferred; sequentially numbered references.
l sequentially numbered references. The basic reference format is: author name, "article name", issue name (italic), vol., no., page, month, year. for example: Y. M. Huang, "pervateture in wireless heterogeneous…", IEEE Journal on Selected Areas, vol. 27, no. 5, pp 34-50, May, 2009.
l brief biographies of authors (50-75 words)
l contact information, including email and mailing addresses
Please note that each submission will normally be approximately 4500 words, with no more than 20 mathematical formulas, accompanied by no more than 10 figures and/or tables.