The field of wireless communications and networking is moving towards the integration of artificial intelligence and machine learning to enhance security, efficiency, and reliability. Recent developments have focused on leveraging large AI models, vision models, and game-theoretic approaches to address challenges in low-altitude wireless networks, Internet of Things, and smart city applications. Notable advancements include the use of large language models for secure communication tasks, vision models for wireless network optimization, and game-theoretic environments for simulating dynamic interactions between UAVs. These innovative approaches have shown promising results in improving communication stability, security, and mapping accuracy.

Noteworthy papers include:

• Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks, which proposes a novel LAM-based optimization framework for secure communication tasks.
• Mamba for Wireless Communications and Networking, which presents a comprehensive overview of Mamba's applications in wireless systems and proposes two application frameworks.
• LiGen: GAN-Augmented Spectral Fingerprinting for Indoor Positioning, which achieves submeter-level accuracy in indoor localization using spectral intensity patterns of ambient light.
• CASH: Context-Aware Smart Handover for Reliable UAV Connectivity on Aerial Corridors, which reduces handover frequency by up to 78% while maintaining low outage probability.