Scenario
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 Both original, unpublished contributions and survey/tutorial types of articles are encouraged.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:
• 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
• 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