IEEE International Conference on Communications
20-24 May 2019 // Shanghai, China
Empowering Intelligent Communications

W14: 4th International Workshop on Advanced PHY and MAC Technology for Super Dense Wireless Networks (CROWD-NET) "AI with Dense Wireless Networks"

W14: 4th International Workshop on Advanced PHY and MAC Technology for Super Dense Wireless Networks (CROWD-NET) "AI with Dense Wireless Networks"

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The next generation of wireless networks, i.e., 5G and beyond, autonomous vehicle networks will be extremely dynamic and complex due to the ultra-densely deployed wireless networks. These introduce many critical challenges for signal processing, network planning and operation, network management, etc. Meanwhile, the generation and consumption of wireless data are becoming increasingly distributed, with the ongoing paradigm shift from people-centric to machine-oriented communications, making the operation of future wireless networks even more complex. To mitigate the complexity of future wireless network operation, new approaches for intelligently utilizing distributed computational resources with improved context-awareness becomes extremely important. In this regard, the edge computing architecture aiming to distribute computing, storage, control, communication, and networking functions closer to end users, have a great potential for enabling efficient operation of future wireless networks. Such promising architectures make the adoption of artificial intelligence (AI) principles, which incorporate learning, reasoning and decision-making mechanism, as natural choices for designing a tightly integrated network.

Scope and Objectives

This workshop aims to gather researchers, regulators, and users to present and debate these challenges through the aspects of advanced PHY and MAC techniques for AI empowered densely populated wireless networks and applications, with the perspective of URLLC, C2X. Specifically, but not exclusively, the workshop addresses the following issues related to super dense wireless networks: 

  • Data-driven dense wireless networking with AI
  • AI for dense wireless networks and coding 
  • AI for mobile edge computing
  • Artificial neural networks for wireless networking
  • Information theoretic limits
  • Learning theory for dense wireless networks
  • Advanced modulation and coding schemes
  • Cooperative communications in AI empowered HetNets
  • Machine learning for C2X networks
  • Machine learning for beyond 5G 

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