Deep Learning for Next-Generation Wireless Networks
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 9600
Special Issue Editors
Interests: B5G networks; network slicing; deep learning; federated deep learning
Interests: connected and autonomous vehicles; Internet of Things; information centric networks
Interests: Internet of Things; Industrial Internet of Things; machine learning and artificial intelligence; mobile computing; security and privacy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning (DL), including deep supervised learning, deep unsupervised learning, and deep reinforcement learning, has been a key enabler in future wireless networks, including 5G and beyond (B5G) networks. DL techniques can not only exploit the massive quantities of data generated by these networks, but can also efficiently and effectively solve a broad range of problems (e.g., decision making, optimization, and prediction in B5G), without the access of mathematical formulations. This capability is helpful for accelerating the design and deployment of the functionality of such networks, especially for situations when mathematical models are either complex to define, or fail to properly formulate the considered problems. However, the application of DL techniques in wireless networks poses a series of problems, such as data communication, model design, model training, model deployment and security protection, which need to be carefully addressed. Further, newly emerging B5G scenarios such as massive machine-type communications (mMTC), extreme ultra-reliable low-latency communications (EURLLC), Industrial Internet of Things (IIoT), unmanned aerial vehicles (UAVs), and joint communication and sensing (JCS) have brought new challenges and research opportunities for the design and optimization of the functional modules in B5Gs, and there is a great deal of room for research exploring strategies for the development DL based solutions to meet the stringent performance requirements for the delay, reliability, and accuracy of these scenarios.
In this Special Issue, we solicit original, high-quality papers in the field of DL-driven B5Gs, potential topics may include but are not limited to:
▪ DL solutions for medium access control layer functionality, including resource allocation, user association, mobility management, etc.
▪ DL for core network management in B5Gs, including network slicing, virtualization, service deployment/migration, etc.
▪ DL for emerging scenarios in B5Gs, including EURLLC, mMTC, UAV, IIoT, JCS, etc.
▪ DL for fog/edge/cloud computing in B5Gs.
▪ AI-powered energy-efficient network orchestration.
▪ Security and privacy for DL-driven wireless communications.
▪ Testbed, experimental evaluations, and real-world applications of DL techniques in wireless communications.
Dr. Bouziane Brik
Dr. Junaid Ahmed Khan
Prof. Dr. Guangjie Han
Guest Editors
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Keywords
- deep learning
- 5G networks and beyond
- IIoT
- UAVs
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