Call for Papers -- Feature Topic, Vol.17, No.8, 2020
Edge Artificial Intelligence in 6G Systems: Theory, Key Techniques, and Applications
As the permeation of artificial intelligence (AI) in wireless applications, some data-driven and computing-intensive services are emerging, such as mobile high-definition AR/VR, and real-time fingertip interactions. Moreover, the application scenarios have been extended to penetrate the vertical industry, which have multi-dimension and diverse quality of service (QoS) requirements. Recently, the research and the development of 6G have been triggered, and much higher QoS requirements of data rate, latency, and connectivity will be raised.
To support the user experience of these services in 6G systems, the procedures of data transmissions and service implementations should be coupled tightly. It requires the fusion of AI and big data to enable the network intelligence, especially at the edge of wireless networks. Therefore, the concept of edge AI is raised to support AI-driven services, and to implement intelligent network management and signal processing. The key idea of edge AI is to support the applications of AI via the coordination of the edge nodes, such as the user equipments and the access points, which can fully explore the potential of fog computing and edge caching capability of the future 6G systems.
Compared with the existing research fields that AI are successfully employed, the implementations edge AI in 6G systems faces some unique challenges: First, from the perspective of data, the data sets are sensed and collected by the edge nodes. The distributions of these data are neither independent nor identical, or unbalanced. Second, from the perspective of learning strategies and models, the existing centralized learning paradigms are not suitable, since the communication costs are extremely high, and there exist critical security issues to transmit the privacy-sensitive user data via wireless channels. Finally, from the perspective of implementations, the blue print of the application scenarios of edge AI is not clear enough, and its potential is still unknown.
Therefore, the target of this feature topic is to will focus on the state-of-art works with respect to the theory, key techniques and applications of edge AI in 6G systems, which can provide efficient solutions for the key challenges. This topic focuses on building a platform to share and discuss recent advances and future trends of edge AI, and to bring academic researchers and industry developers
The feature topic solicits the submissions of manuscripts that present original novel works of the theory, key techniques, applications, and standardization activities with respect to edge AI in 6G systems. Technique papers that presents analytical results, algorithms, protocols, implementations, experiment results and prototypes, are welcome. Survey papers or visionary articles indicating future directions from different perspectives are also encouraged.
Submission Deadline: March 15, 2020
Acceptance Notification (1st round): May 1, 2020
Minor Revision Due:
June 1, 20
Final Decision Due: June 15, 2020
Final Manuscript Due: June 25, 2020
Publication Date: August 15, 2020
Zhongyuan Zhao, Beijing University of Posts & Telecommunications, China
Zhiguo Ding, The University of Manchester, UK
Mugen Peng, Beijing University of Posts & Telecommunications, China
Topics include (but not limited to):
l Information theory and computation theory of ENI
l ENI-enabled 6G network architecture
l Distributed machine learning/deep learning paradigms for ENI
l AI-enabled physical-layer signal processing techniques for 6G
l High dynamic network orchestration and slicing based on ENI
l ENI-enabled radio resource management for 6G
l ENI-based large-scale distributed coordination schemes
l Integrations of communication, computation, and caching for ENI in 6G
l Data sensing, collection, and analysis for ENI
l Security of ENI in 6G
l Implementations of ENI in vertical industry
l Standardization of AI and fog/edge computing in 6G
l Advanced evaluation tools and data sets for ENI
l Prototype, demo, and test-bed
This feature topic “Edge Artificial Intelligence in 6G Systems: Theory, Key Techniques, and Applications” invites submissions of original, previously unpublished technical papers and visionary articles exploring the theory, key techniques, and applications of edge AI in 6G. All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical content. 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 about 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, accomplished by up to 10 figures and/or tables.