The field of low-altitude wireless networks and edge computing is rapidly evolving, with a focus on improving task offloading, resource allocation, and security. Researchers are exploring innovative solutions, such as graph attention diffusion, reinforcement learning, and generative AI, to optimize network performance and efficiency. Notable papers in this area include proposals for joint task offloading and resource allocation, resilient communication for avalanche response, and RL-based adaptive task offloading. These advancements have the potential to significantly enhance the capabilities of low-altitude wireless networks and edge computing systems.
Noteworthy papers:
- Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion: proposes a graph attention diffusion-based solution generator for joint task offloading and resource allocation.
- Generative AI-enhanced Low-Altitude UAV-Mounted Stacked Intelligent Metasurfaces: investigates a novel UAV-mounted stacked intelligent metasurfaces assisted communication system and proposes a generative AI-based hybrid optimization algorithm.
- Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors: proposes a vision-aided integrated sensing and communication framework for UAV-assisted access systems, utilizing De-Diffusion models to extract compact semantic tokens.