AI-Empowered Wireless Communications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2929
Special Issue Editors
Interests: caching strategies for wireless video streaming; machine learning in wireless communication systems; compressed sensing in wireless communications; cognitive radio; physical layer security; 5G mobile systems
Special Issues, Collections and Topics in MDPI journals
Interests: wireless communications system; wireless distributed learning
Interests: wireless communications; machine learning; signal processing
Interests: wireless security; machine learning; interference management; channel quantization; game theory; signal processing techniques in wireless communication systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in computing hardware and machine learning algorithms have brought innovative development in a wide range of technologies, such as recommendation systems, self-driving vehicles, and chatbots, by addressing intractable challenges with traditional approaches. In this regard, the machine learning technique is expected to play an important role for dealing with the ever-increasing demand for connectivity in future wireless networks. There is a critical need to develop intelligent communication techniques and communication-aware machine learning techniques for mobile and sensor networks.
The objective of this Special Issue is to address, discuss, and present novel machine learning-based wireless communication techniques for future wireless networks. The topics of the Special Issue include, but are not limited to the followings:
- Machine learning for Internet of things (IoT) and massive connectivity;
- Machine learning for ultra-reliable and low latency communications (URLLC);
- Machine learning for massive MIMO;
- Machine learning driven novel waveform designs;
- Machine learning for distributed communications;
- Machine learning for communication resource allocation;
- Machine learning for user scheduling;
- Communication-aware federated learning.
Dr. Jun-Pyo Hong
Dr. Jaeyoung Song
Dr. Jinho Kang
Dr. Jung Hoon Lee
Guest Editors
Manuscript Submission Information
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