Innovative AI Approaches for Emerging 6G Wireless and Computing Systems

Dear Colleagues,

The future of wireless networks and computing systems is looking towards incorporating Artificial Intelligence (AI) to enhance their capabilities and performance. These AI-based solutions are being developed to address the challenges of providing ultra-reliability and ultra-low latency while supporting a wide range of services, such as holographic-type communication, the Tactile Internet, virtual reality (AR/VR), autonomous driving, vehicle-to-everything (V2X), the internet of things (IoT), smart grids, the metaverse, remote surgery, free-space optics and precise agriculture. However, due to the dynamic nature of these networks, which consist of heterogeneous small cells, traditional AI-based solutions designed to fit all scenarios and devices are not practical. In addition, the rapid growth in the number of devices like smartphones, sensors, and vehicles, along with the complexity of network infrastructures like cellular and WiFi, and the variety of services they provide, is making it increasingly difficult to control and manage these systems. Therefore, innovative AI techniques are being researched as a more suitable approach to future wireless networks. These solutions employ various algorithms, such as statistical learning theory, deep learning, convex optimization, multi-armed bandits, game theory, and prediction techniques, to adapt to the changing wireless communication environment and make smart, low-complexity decisions. These algorithms can be applied to various wireless communication systems, including millimeter-wave/terahertz communications, device-to-device communications, non-orthogonal multiple access systems, physical-layer security, unmanned aerial vehicle communications, cognitive radio systems, reconfigurable intelligent systems, and wireless power transfer.

Thus, the aim of this Special Issue is to present the benefits of advanced AI-based solutions for the challenges associated with emerging wireless and computing systems, and to do so by attracting the submission of many high-quality papers.

In this Special Issue, original research articles and reviews that address the main research challenges associated with the application of online-learning-aided solutions to the handling of future 6G networks are welcome. Research areas may include (but are not limited to) the following:

• Innovative AI techniques for satellite-assisted communications;
• Innovative AI techniques for distributed resource provisioning;
• Advanced AI-based solutions for mmWave/THz channel estimation;
• Advanced AI-based solutions for UAV-based wireless communications;
• Advanced AI-based solutions for device-to-device (D2D) communications;
• Advanced AI-based solutions for cognitive radio systems;
• Advanced AI-based solutions for 6G-enhanced IoT networks;
• Advanced AI-based solutions for wireless power transfer systems;
• Advanced AI-based solutions for mobile/edge computing;
• Innovative AI-based solutions for V2V, V2I, and V2X scenarios;
• Innovative AI techniques for network resource management;
• Innovative AI techniques for physical-layer wireless communication;
• Advanced AI methods for transport-layer congestion control;
• Innovative AI techniques for computation offloading;
• Machine learning for traffic engineering;
• Innovative AI techniques for optical communications ;
• Innovative AI techniques for reconfigurable intelligent surface design ;
• Innovative AI techniques for network slicing;
• Innovative AI techniques for quality-of-service (QoS) assurance;
• Innovative AI solutions for RIS-aided wireless communication networks;
• Advanced AIs for wireless resource allocation and mobility management in 6G networks;
• Innovative AI methods for energy harvesting, power control, and wireless power transfer channel estimation and prediction solutions;
• AI-based multi-access and modulation (NOMA, OTFS, SCMA, etc.);
• AI solutions for 6G ultra-high reliability and low-latency communications (URLLC);
• AI-aided solutions for interference management, and cancellation techniques in 6G networks;
• Communication-efficient machine learning techniques (such as transfer learning, federated learning, online un/supervised learning, reinforcement learning, and meta-learning).

We look forward to receiving your contributions.

Dr. Sherief Hashima
Dr. Haithem Khallaf
Guest Editors

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• AI
• 6G
• vehicle to everything (V2X)
• Internet of Things (IoT)
• D2D
• NOMA