We are happy to announce that MLICOM Conference Registration is now open!
News! Keynote for MLICOM 2017 announced:
Haijun Zhang, Full Professor (University of Science and Technology Beijing, China)
Title: Intelligent Ultra Dense Networks: Principles and Technologies
Along with the fast developing of mobile communications technologies, the amount of high quality wireless services is required and increasing exponentially. According to the prediction of Cisco VNI Mobile Forecast 2016, Global mobile data traffic will increase nearly eightfold between 2015 and 2020, and mobile network connection speeds will increase more than threefold by 2020. Hence, there are still big gap between the future requirements and current communications technologies, even using 4G/5G. How to integrate the limited wireless resources with some intelligent algorithms/schemes and boost potential benefits are the interests of the conference. As an emerging discipline, machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence, and explores the study and construction of algorithms that can learn from and make predictions on complicated scenarios. In communication systems, the previous/current radio situations and communication paradigms should be well considered to obtain a high quality of service (QoS), such us available spectrum, limited energy, antenna configurations, and heterogeneous properties. Machine learning algorithms facilitate complicated scenarios analysis and prediction, and thus to make an optimal actions in OSI seven layers. We hope the integrating of machine learning algorithms into communication systems will improve the QoS and make the systems smart, intelligent, and efficient. We invite high quality original research papers describing recent and expected challenges or discoveries along with potential intelligent solutions for future mobile communications and networks. We welcome both theoretical and experimental papers. We expect the papers of the conference to serve as valuable references for a large audience from both academia and industry. Both original, unpublished contributions and survey/tutorial types of articles are encouraged.
Topical Scope of the Conference:
This conference focuses on applying machine learning algorithms in communication systems, in order to improve the quality of service and make the systems smart, intelligent, and efficient. The topics of interest for the conference include, but are not limited to:
· Intelligent cloud-support communications
· Intelligent spectrum (or resource block) allocation schemes
· Intelligent energy-aware/green communications
· Intelligent software defined flexible radios
· Intelligent cooperative networks
· Intelligent antennas design and dynamic configuration.
· Intelligent Massive MIMO communication systems
· Intelligent positioning and navigation systems
· Intelligent cooperative/distributed coding
· Intelligent wireless communications
· Intelligent wireless sensor networks
· Intelligent underwater sensor networks
· Intelligent satellite communications
· Machine learning algorithm & cognitive radio networks
· Machine learning and information processing in wireless sensor networks
· Data mining in heterogeneous networks
· Machine learning for multimedia
· Machine learning for IoT
· Decentralized learning for wireless communication systems
· The event is endorsed by the European Alliance for Innovation, a leading community-based organisation devoted to the advancement of innovation in the field of ICT.
· All accepted papers will be published by Springer and made available through SpringerLink Digital Library, one of the world's largest scientific libraries.
· Proceedings are submitted for inclusion to the leading indexing services: Elsevier (EI), Thomson Scientific (ISI), Scopus, Crossref, Google Scholar, DBLP.
· The authors of the best papers will be invited to submit an extended version of their work through a special issue of Mobile Networks and Applications (IF=1.538) and a special issue of IEEE Access (IF=1.27).
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