A Joint Sensing, Communication, and Task Offloading Framework for Vehicular Metaverse

Recently, metaverse-empowered wireless systems have gained significant interest in the research community because of the appealing features of self-sustainability and proactive learning. Self-sustainability allows a system to run with the least amount of assistance from network administrators, whereas proactive learning allows for the development of machine learning models prior to user requests. As a result, the idea of the metaverse in vehicular networks can be used to enable a variety of applications (e.g., infotainment and collision avoidance) for a massive number of autonomous vehicles. However, metaverse-empowered vehicular networks are challenging to implement due to computing (i.e., at autonomous cars and network edges) and communication resource constraints. We present a novel framework for joint sensing, communication, and task offloading for vehicular networks empowered by the metaverse in order to address these issues. We formulate a problem to minimize a cost function that takes into account transmission latency, sensing, and transmission energy. Sensing interval, resource distribution, and task offloading are all optimized for minimizing the cost. We use convex optimization for the sensing problem while a decomposition-relaxation-based approach is used for joint resource allocation and task offloading. Finally, numerical results are provided to support the proposed scheme.