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

Recently, the research community has shown overwhelming interest in metaverse-enabled wireless systems, due to their attractive proactive learning and self-sustainability attributes. Proactive learning enables machine learning models to be trained before user requests, while self-sustainability allows a system to function with the least amount of assistance from the network administrators/users. Because of these features, one can use a metaverse to enable various applications (e.g., entertainment and collision avoidance) in intelligent transportation systems. However, the limitations of computing processing power (e.g., in autonomous cars) and communication resources make implementing metaverse-empowered vehicular networks challenging. Motivated by these facts, we present a new framework for cooperative sensing, communication, learning, and task offloading for vehicular networks enabled by the metaverse. Subsequently, we formulate a cost-function minimization problem that accounts for transmission energy and transmission latency. The cost is minimized by optimizing task offloading, wireless resource allocation, transmit power allocation, and sensing interval. We employ a decomposition-based strategy for simultaneous resource allocation, task offloading, sensing interval optimization, and transmit power allocation. Due to the combinatorial nature of the resource allocation and task offloading problems, matching-based solutions are used. For sensing interval optimization, convex optimization is used. On the other hand, due to the nonconvex and continuous nature of the transmit power allocation problem, a proximal term is introduced into the objective function to approximate it as a convex objective function, which is then solved using a convex optimizer. To gain further insights, the proposed scheme is supported by extensive numerical results.