Enhancing Metaverse Fidelity and Freshness via GAI-Aided Semantic Communication in Low-Altitude IoT Networks

The rapid iteration of artificial intelligence (AI) and 5G technologies has created essential foundations for Metaverse, easing bottlenecks in content generation, real-time interaction, and large-scale deployment. Integrating unmanned aerial vehicle (UAV) with semantic communication (SemCom) further provides a lightweight sensing and uplink solution, but semantic compression and bias may degrade fidelity. Generative AI (GAI), with powerful contextual modeling and generation capabilities, helps mitigate these issues. Based on this, this paper proposes a GAI-aided SemCom architecture and evaluates the feasibility of applying it to low-altitude internet of things (IoT) networks. We build evaluation models for representative image compression and GAI-aided SemCom techniques, replacing traditional task assumptions with realistic cases. Then, taking UAV networks as an example, we design a path planning and two task allocation schemes. Specifically, the UAV path planning strategy together with a greedy task allocation scheme improves the Metaverse update frequency and ensures the data freshness, while a parametrized deep Q-network (PDQN) task allocation scheme jointly optimizes Metaverse fidelity and freshness through mixed action selection, i.e., preprocessing methods (discrete) and compression size (continuous). Extensive analysis and numerical results corroborate that GAI-aided SemCom technology has practical significance in low-altitude IoT networks. Through the collaboration of multiple data processing schemes and the rational resource allocation, the fidelity and freshness of Metaverse can be greatly improved.