Generative AI-Aided Vertical Handover Decision in SAGIN for IoT With Integrated Sensing and Communication

As an advanced form of IoT technology, integrated sensing and communication (ISAC) deeply integrates communication and perception, enhancing the performance and application range of IoT. At the same time, the space-air-ground integrated network (SAGIN) provides a wider and more efficient connection and information processing support for both. However, the highly dynamic and time-varying characteristics of SAGIN lead to more frequent vertical handovers among heterogeneous wireless networks, which seriously affects the continuity and reliability of services. This motivates us to explore an effective vertical handover method in SAGIN to guarantee the quality of network service. The issue is a typical complex and high-dimensional problem with its online and dynamic characteristics, which provides a particularly favorable scenario for the adaptability of the diffusion model (DM). Accordingly, we propose a novel vertical handover decision algorithm with the aid of DM. First, we innovate a novel vertical handover analytical model that describes handover jitter, load difference, and handover robustness. Then we formulate it as a multiobjective optimization problem. Next, inspired by Generative AI (GAI), we propose a DM-based GAI-empowered handover decision (DGHD) algorithm to capture the time-varying and high-dimensional environments and generate optimal vertical handover decisions. Subsequently, the policy network of multiagent proximal policy optimization (MAPPO) is replaced with the proposed DGHD for addressing environmental uncertainty and enhancing efficiency. Finally, the simulations exhibit that our proposed algorithm outperforms existing algorithms.