Robust Information Delivery and Energy Efficiency Maximization in D2D-Based V2X Network

Intelligent transportation systems (ITS) are transforming modern mobility, with vehicle-to-everything (V2X) communication emerging as a critical technology for enhancing transportation safety and efficiency. However, the dynamic nature of vehicular networks presents significant challenges, including unreliable channel state information and limited spectrum resources. These limitations can compromise the reliable and low-latency transmission of safety-critical data. To address these challenges, this paper proposes a robust approach for device-to-device (D2D)-based V2X communication networks, focusing on jointly optimizing channel reuse and power allocation to maximize information transmission success rate (SR) and average energy efficiency (AE). A two-step strategy is developed: Firstly, a long-timescale Kuhn-Munkres (LTKM) algorithm is devised to improve channel efficiency through intelligent channel reuse decisions. Secondly, the power allocation problem is modeled as a Markov decision process (MDP) and resolved using a proximal policy optimization (PPO)-based algorithm, enhancing the network’s robustness to time-varying vehicular network scenario. Simulation results demonstrate the effectiveness of our proposed method. Compared to the original Kuhn-Munkres algorithm, the signaling overhead of our approach is reduced by approximately 82%. Furthermore, compared to three benchmark schemes, our approach improves overall performance by approximately 11%, 20%, 33%, and 61%, respectively. Moreover, our approach exhibits more stable performance under different vehicle speeds, which further highlights the robustness of the proposed method.