Machine Learning for Wireless Communications
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 15483
Special Issue Editors
Interests: machine learning for wireless communications; statistical signal processing; Internet of Things (IoT); 6G; spectrum sensing and sharing in cognitive radio (CR) networks
Interests: machine learning for wireless communications; statistical signal processing; Internet of Things (IoT); 6G; spectrum sensing and sharing in cognitive radio (CR) networks
Interests: wireless communication; integrated access and backhaul; orthogonal time frequency space; deep learning; dynamic spectrum access; privacy and security; massive mimo; anti-jamming
Interests: wireless communications; network security; privacy preservations; machine learning
Interests: delay-Doppler communications; integrated sensing and communications; orthogonal time frequency space
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Special Issue Information
Dear Colleagues,
The constantly emerging new applications of communications, such as truly immersive multisensory extended reality (XR), wearable devices, unmanned aerial vehicles (UAVs), etc., are progressively improving the quality of our daily life. Meanwhile, these new communication applications are generating a large amount of data traffic with heterogeneous quality-of-service (QoS) requirements. As such, there is an emerging need to integrate machine learning technologies into designing, planning, and optimizing future wireless communications. Recently, modern machine learning technologies, especially deep learning techniques, have been proven to have the powerful data-driven capability to facilitate wireless communications in a variety of scenarios, such as channel modeling, channel estimation, signal detection, resource allocation, network optimization, etc. This Special Issue aims to bring together advances in the research on machine learning for wireless communications across a broad range of applications.
Topics of interest include but are not limited to the following:
- Machine learning (including deep learning, deep reinforcement learning, etc.) for signal detection, classification, compression;
- Machine learning for spectrum sensing, localization, and positioning;
- Machine learning for channel modeling, estimation, and prediction;
- Machine learning for resource allocation and network optimization;
- Machine learning for new emerging applications toward 6G (including intelligent reflection surface, unmanned aerial vehicles, the Internet of Things, etc.)
- Performance analysis and evaluation of machine learning empowered wireless communication systems;
- Machine learning for vehicular networks;
- Distributed machine learning/federated learning and communications.
Dr. Chang Liu
Dr. Shihao Yan
Dr. Qingqing Cheng
Dr. Minghui Min
Dr. Weijie Yuan
Guest Editors
Keywords
- machine learning for wireless communications
- deep learning
- deep reinforcement learning
- neural network
- 6G communications
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