IoV-Oriented Integrated Sensing, Computation, and Communication: System Design and Resource Allocation

In future mobile communication systems, the deep integration of communication, sensing, and computation has become a new trend. This article paper designs an integrated sensing, computation, and communication (ISCC) system for internet of vehicles (IoV) by leveraging mobile edge computing (MEC) and integrated sensing and communication (ISAC). Specifically, a collaborative sensing data fusion architecture is proposed, where vehicles collaborate with road side unit (RSU) to address the issue of limited sensing range for a single vehicle. Furthermore, the randomness of collaborative sensing tasks arrival in real-world traffic scenarios is simulated, with communication, sensing, and computation being modeled respectively. The joint optimization of wireless and edge computing resources in the ISCC system aims at maximizing the completion rate of collaborative sensing tasks while ensuring system service delay. To address the mixed-integer nonlinear optimization problem, a resource allocation scheme based on deep reinforcement learning (DRL) is developed, which enables adaptive allocation of wireless and edge computing resources through online learning. Simulation results show that agent of DRL can learn a smart resource allocation strategy from the interactive environment, and achieve better performance than conventional resource allocation schemes.