Integrating Deep Learning for Network Performance: Enhancing network reliability and performance, particularly under adversarial conditions, using deep learning techniques.

Reinforcement Learning for Network Optimization: Applying reinforcement learning to optimize routing and resource allocation in cognitive radio networks.

Localization in IoT and Vehicular Networks: Enhancing the localization capabilities in IoT and vehicular networks for better tracking and navigation.

Digital Twin Technology for Network Optimization: Exploring digital twin technology for optimizing space-air-ground integrated networks and vehicular edge computing.

IoT and URLLC Networks: Enhancing ultra-reliable low-latency communications (URLLC) in IoT networks using digital twin and active reconfigurable intelligent surfaces.

Reconfigurable Intelligent Surfaces (RIS): Investigating the use of RIS for enhancing energy efficiency and network performance.

Ultra-Reliable Low-Latency Communications (URLLC): Developing strategies to ensure ultra-reliable and low-latency communication in critical applications.

Rate-Splitting Multiple Access (RSMA): Exploring RSMA techniques for efficient spectrum utilization and improved network capacity.

Extreme URLLC (xURLLC): Addressing the challenges and opportunities in extending URLLC capabilities to broader scenarios and applications.

Integrated Sensing and Communication (ISAC): Exploring the integration of sensing and communication functionalities to enhance wireless network capabilities.

Quantum Machine Learning for Network Optimization: Applying quantum machine learning techniques to optimize network operations and resource management.

Quantum-Enhanced Algorithms: Developing quantum-enhanced algorithms to tackle complex problems in wireless communications and networking.