Causal-Inference-Driven Communication-Enhanced Computation Offloading for IRS-Assisted IoV

Abstract

In the mobile edge computing (MEC)-enabled Internet of Vehicle (IoV) networks, the communication quality between vehicles and edge servers during task computation offloading is vulnerable to degradation caused by obstacle interference, thereby reducing user quality of service. The Intelligent Reflecting Surface (IRS) is a passive and programmable device, can be installed on the exterior of buildings to reflect and enhance signals through phase shift adjustment. However, a coupling relationship exists between IRS phase control and task offloading decisions. Optimizing them separately by decoupling fails to reflect the highly coupled and dynamic interactions in real systems. This may lead to a mismatch between offloading decisions and channel conditions, making it difficult to achieve optimal system performance in real-world environments. To address this challenge, we propose a joint optimization method for IRS phase control and task computation offloading based on causal inference (OITC). Specifically, we first analyze and model the dynamic channel between vehicles and the IRS. Then, causal inference is used to verify the causal relationship between IRS phase control and task computation offloading. Moreover, we combine causal inference with the Multi-agent Proximal Policy Optimization (MAPPO) algorithm to jointly optimize IRS phase control and task offloading decisions. Ultimately, extensive experiments demonstrate that the proposed algorithm outperforms traditional multi-agent deep reinforcement learning (MADRL) methods in terms of communication quality, system delay, and energy consumption.