Scope
Recent developments in artificial intelligence (AI) and machine learning, especially in deep learning, has stimulated growing interests to incorporate AI and machine learning into communication systems and networks. While some researchers have advocated applying deep learning tools to communication system (especially receivers) design, others are doubtful as to how much benefits these tools can offer. On one hand, communication systems have been designed and optimized by generations of dedicated efforts for bandwidth, power, and complexity efficiency, and reliability, leaving little room for improvements in most cases. On the other hand, deep learning enabled networks, supported by results such as universal approximation theorem, seem to promise a new and simple design regime where near optimal performance can be achieved by merely using certain ready to use deep learning modules, applying them to communication design problems, and tuning them based on the easily generated training data. The deep learning based approach may offer some new design approaches for traditionally difficult signal processing tasks in communications and networks.
This conference is meant to stimulate the debate and provide a forum for researchers working in related problems to exchange ideas and recent results (both positive and negative ones) in applying artificial intelligence to communications and networks. Both supervised learning and unsupervised learning, reinforcement learning, and recent developments in generative adversarial networks, and game-theoretic setups are also of great interests. The topics will include, but are not limited to the following:
Topics
We seek novel, innovative, and exciting work in areas including but not limited to:
- AI-enabled Mobile Networks Design
- Deep Learning/Machine Learning in Wireless Communications and Networking
- Deep Learning/Machine Learning in Cognitive Radio
- AI-based Network Intelligence for IoT
- AI-enabled Ultra-Reliable and Low-Latency Communications
- AI-enabled Network Softwarization and Virtualization
- Deep Learning/Machine Learning in Big Data enabled Wireless Networking
- Low-power Architecture with Deep Learning for Wireless Communications
- Advanced Deep Learning/Machine Learning Algorithms for Wireless Networks
- AI-based Adaptive and Dynamic Network Slicing
- New Network Pricing Models based on Deep Learning/Machine Learning
- The Design of AI-enabled Hardware for Communications System
- Novel design of deep-learning and convolutional neural network approaches for wireless system applications and services.
- Novel design of machine-learning and pattern recognition algorithms for wireless communication technologies.
- Applications of AI for optimizing wireless communication systems, including channel models, channel state estimation, beamforming, code book design and signal processing.
- Applications of AI for 5G wireless transmission technologies, including coordinated multiple-point transmission/reception, large scale antenna array, and multi-hop relay.
- Applications of AI for 5G mobile management, including user association, handoff strategy, and backhaul technology.
- Applications of AI for 5G resource management, including spectrum resources, energy sources, cloud resources, computing resources, and communication infrastructure.
- The analysis and prediction of 5G network behavior via AI technologies, including the multi-media traffic load, network overhead, and network collision.
- Evaluating the scope for and potential limitations of AI solutions in wireless communications.
Publication
All registered papers will be submitted for publication by Springer and made available through SpringerLink Digital Library.
Proceedings will be submitted for inclusion in leading indexing services, Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL).
Authors of selected best accepted and presented papers will be invited to submit an extended version to
All accepted authors are eligible to submit an extended version in a fast track of:
Paper submission
Papers should be submitted through EAI ‘Confy‘ system, and have to comply with the Springer format (see Author’s kit section).
Authors are encouraged to submit full papers presenting new research related to the theory or application of artificial intelligence and machine learning technologies to communications and networks. Submitted papers must not have been published elsewhere nor currently be under review by another conference or journal. All papers will be reviewed by Technical Program Committee members and other experts active in the field to ensure high quality and relevance to the conference.
Important dates
Full Paper Submission deadline
15 February 2019
Notification deadline
15 March 2019
Camera-ready deadline
1 April 2019
Start of Conference
25 May 2019
End of Conference
27 May 2019