Energy-Efficient Joint Computation Offloading and Resource Allocation Strategy for ISAC-Aided 6G V2X Networks

Abstract

Integrated sensing and communications (ISAC) is a pillar technology of 6G to enable intelligent environment awareness and hardware cost efficiencies. In ISAC-aided 6G vehicle-to-everything (V2X) networks, the perception data fusion from multiple sources can make a reliable and efficient data sharing to guarantee driving safety. However, the contradiction between task delay requirement and energy consumption becomes more and more prominent with the growth of computing task amount. Therefore, this paper proposes a joint computation offloading and resource allocation strategy to build greener V2X networks with mobile-edge computing (MEC) and ISAC technologies. A data fusion architecture for cooperative perception is first introduced to support fusing massive perception data from wireless infrastructures and vehicles. Then, a minimization problem of queuing latency is formulated with long-term latency and energy consumption constraints for data fusion computing tasks. The problem is further reformulated by the Lyapunov optimization method to transfer delay and energy constraints into queue stability problems. Finally, a joint computation offloading and resource allocation (JCORA) scheme is proposed to obtain the optimal computation offloading and resource allocation decision, achieving the balance between task delay and energy consumption. Extensive simulations validate the effectiveness of the proposed strategy compared with other baseline schemes.