Traditional communication focuses on bit accuracy, while semantic communication improves efficiency by considering the meaning of the data. This paper introduces semantic communication to intelligent transportation systems (ITS), specifically image tasks in vehicle-to-everything (V2X) networks. We propose an adaptive signal-to-noise ratio (SNR) image semantic communication model (ASISC) to address the dynamic nature of V2X environments. An evaluation of the real utility function (ERUF) based on task performance is proposed, which takes into account the semantic transmission rate and the semantic energy consumption. We define the semantic importance scores (SIS) to quantify the complexity of image content. To maximize the ERUF and SIS for image transmission, an optimization problem is formulated to promote high SIS image transmission in V2X networks. In particular, to address the interpretability challenge of neural networks, we propose an independent univariate approach consisting of a content equalization sampling step and an approximate modeling step, to transform the original optimization problem into decoupled power allocation and transmission order subproblems. A ternary search algorithm is used for power allocation, and a distance-based transmission order scheme (DistO) is proposed to give preferential treatment to high SIS image tasks. Simulation results on the CIFAR10 dataset demonstrate that our method outperforms the benchmark schemes, especially in high SIS scenarios, and the transmission efficiency is greatly improved. The proposed scheme is easy to implement and exhibits excellent performance in the V2X networks.

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.

Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Paul Pu Liang, Terrance Liu, Anna Cai, Michal Muszynski, Ryo Ishii, Nick Allen, Randy Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.