Intelligent Radio: When Artificial Intelligence Meets Radio Network
摘要截稿:
全文截稿: 2019-05-31
影响因子: 11.391
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 1区
• 小类 : 计算机:硬件 - 1区
• 小类 : 计算机:信息系统 - 1区
• 小类 : 工程:电子与电气 - 1区
• 小类 : 电信学 - 1区
Overview
Cognitive Radio has been researched for more than ten years since 1999. The intelligence introduced in cognitive radio pays primary attention on solving spectrum access problems. The rising of the fifth generation (5G) communication system extends radio services to various vertical industries, which renders more complexity and thus challenges to wireless communications. The recent advances in artificial intelligence (AI), including machine learning, data mining, and big data analytic hold significant promise for addressing many complex problems in wireless networks. By extending the intelligence from spectrum access, to network management and service orchestration, Cognitive Radio is on the way to evolve to Intelligent Radio.
AI is defined as any process or device that perceives its environment and take actions that maximize the chances of success for some predefined goal. It is appropriate to apply AI technologies to tackle accurate channel modeling, optimized physical layer design, flexible spectrum access, and complicated network deployment, automation, optimization, and management issues in the wireless domain. Emerging machine learning approaches and big data analytic technologies have also brought us excellent opportunities to further investigate the essential and so far unexplored characteristics of wireless networks, and to help make breakthrough in wireless communications, via novel radio and networking techniques, including new architectures as well as sophisticated algorithm and protocol designs.
The emerging Intelligent Radio has drawn particular attention to inter-disciplinary approaches from wireless communications and the AI research community. This Feature Topic (FT) will bring together leading researchers and developers from both industry and academia. The purpose of this FT is to provide the academic and industrial communities an excellent venue to present and discuss technical challenges and recent advances related to AI for wireless communications and networking. The topics of interest include, but are not limited to:
1. Wireless system design and optimization o AI for channel measurement, modelling and estimation
New collaborative spectrum sensing enhanced by AI
AI for wireless transmission technologies, including antenna array, beamforming, code book design and signal processing
AI for system design, including multi-connectivity, and multi-hop relay
AI/machine learning for resource allocation and medium access control
Protocol design and optimization using machine learning
2. Network management
AI for mobility management, including user association, handoff strategy, and backhaul technology
Energy-efficient resource allocation via AI/machine learning algorithms
AI for radio network slicing, and resource allocation for shared/virtualized networks
AI for network analytics and diagnosis
Machine learning, data mining, statistical modeling and big data analytics for network management
Evaluating potential limitations of AI solutions for networking
3. Network applications and services
Novel deep-learning and convolutional neural network approaches for wireless system applications and services
Network architecture and optimization for AI/machine learning applications
Machine learning for media and entertainment in wireless networks
AI for network security, reliability, and safety.
AI for edge caching and storage
AI for convergence of communications and computing
AI for localization and positioning
4. Network automation
Machine learning in network control & automation
Proactive network monitoring architecture
Self-learning, self-organizing and predictive maintenance protocols and algorithms
Open-source AI algorithms and software for networking