Online Learning Aided Solutions for 6G Wireless Networks

Dear Colleagues,

The evolving Sixth Generation (6G) wireless networks will provide 100 to 1000 Gbps rates and ultra-low latency of 1 millisecond using native, embedded Artificial Intelligence (AI) capability to support myriad services, such as Holographic Type Communication (HTC), tactile Internet, remote surgery, etc. However, these services demand ultra-reliability, which is highly impacted by the dynamically changing environment of 6G heterogeneous tiny cells, whereby static AI-based solutions fitting all scenarios and devices are impractical. Therefore, online learning-based solutions are proper ones for future 6G networks due to their self-decision making and adaptability to the time-varying wireless communication environment. This can be done by smartly leveraging online-based algorithms such as statistical learning theory, online convex optimization (OCO), multi-armed Bandits (MABs) with their different categories, game theory, and online prediction techniques, to carefully handle different 6G challenges. This talented low cost/fast converging learning methodology motivates researchers and practitioners to apply it and bound its performance in various future wireless communication systems, including Millimeter-Wave/TeraHertz (mmWave/THz) communications, D2D communications, NOMA based systems, Physical Layer Security (PLS), Unmanned Aerial Vehicles (UAV) communications, Cognitive Radio (CR) systems, Reconfigurable Intelligent Systems (RIS), Wireless Power Transfer, etc. Online learning-based algorithms, including online supervised learning, online unsupervised learning, reinforcement learning like MABs, meta-learning, transfer learning, federated learning, and adaptive learning can provide smart/low complexity solutions for learning the 6G channel, optimizing the latency, finding the best neighbor for relaying the BS signal, finding the best device in D2D system, optimizing the trajectory of the UAV, optimizing the performance of RIS-aided wireless communications, optimizing beam directions in mmWave/THz system, improving wireless power transfer, etc. Thus, the focus of this SI is to show the positivity of online learning-based solutions for the challenges of future 6G wireless communication networks by attracting a lot of high-quality submissions.

This Special Issue (SI) is soliciting original technical papers addressing the main research challenges in the direction of applying online learning aided solutions for handling future 6G networks, including, but not being limited to the following topics:

• Online learning-based solutions for mmWave/THz channel estimation.
• Online learning-based solutions for UAV -based wireless communications.
• Online learning-based solutions for device-to-device (D2D) communications.
• Online learning-based solutions for cognitive radio systems.
• Online learning-based solutions for 6G enhanced IoT networks.
• Online learning-based solutions for wireless power transfer systems.
• Online learning-based solutions for mobile/edge computing.
• Online learning-based solutions for V2V, V2I, and V2X scenarios.
• Online learning-based solutions for RIS-aided wireless communication networks.
• Online-based wireless resource allocation and mobility management in 6G networks.
• Online aided energy harvesting, power control, and wireless power transfer channel estimation and prediction solutions.
• Self-learning-based multi-access and modulation (NOMA, OTFS, SCMA, etc.).
• Online learning solutions for 6G ultra-high reliability and low-latency communications (URLLC).
• Online learning-based solutions for Interference avoidance, management, and cancellation techniques in 6G networks.
• Self-learning aided new communications and network technologies in 6G, such as visible light communication (VLC), optical networks, and aerial access networks.
• Online learning solutions for efficient computation offloading technologies in 6G networks.
• Online learning-based solutions for wireless spectrum sensing, localization, and signal processing in 6G networks.
• Communication-efficient online learning techniques (such as transfer learning, federated learning, online un/supervised learning, reinforcement learning, and meta-learning).
• Online enhanced security and privacy issues in 6G communication networks.

Dr. Sherief Hashima
Dr. Ehab Mahmoud Mohamed
Dr. Mostafa Fouda
Guest Editors

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• 6G
• online learning
• MAB
• D2D communications
• CR
• UAV communications
• RIS
• NOMA
• V2X communications