Holographic Counterpart Computation Offloading via Reconfigurable Intelligent Surfaces in VEC Consumer Electronics

Abstract

In vehicular edge computing (VEC) Consumer Electronics networks, the integration of holographic counterpart technology presents significant challenges due to its stringent requirements for high data transmission rates and communication reliability. Traditional task offloading methods, constrained by suboptimal communication link quality and energy limitations, are inadequate to meet these demands. This paper introduces a groundbreaking system that synergistically combines wireless power transfer (WPT) and reconfigurable intelligent surfaces (RIS) to significantly enhance both communication performance and computational efficiency. Leveraging deep reinforcement learning (DRL), our system achieves joint optimization of task offloading strategies and resource allocation. Departing from conventional dynamic RIS designs, we implement a fixed phase shift matrix approach, which not only simplifies system implementation but also reduces computational complexity, thereby enhancing both task offloading efficiency and system stability. Extensive simulation results demonstrate that our optimized RIS-assisted approach achieves a remarkable 38.30% improvement in computational rates compared to non-RIS schemes and a 4.83% enhancement over random-phase RIS configurations. These substantial improvements highlight the transformative potential of RIS in boosting computation rates and providing robust solutions for high-demand task offloading scenarios. Our innovative system design represents a significant advancement in intelligent vehicular networks and edge computing technologies, offering substantial application potential for holographic projection task offloading in next-generation vehicular systems.