# Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2411.04672 · HTML: https://mlsanity.com/paper/2411.04672/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 71 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 30 of 33)

- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [Semantic-preserved Communication System for Highly Efficient Speech Transmission](https://mlsanity.com/paper/2205.12727/) (2022)
- [Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01219/) (2022)
- [High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks](https://mlsanity.com/paper/2208.01890/) (2022)
- [Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method](https://mlsanity.com/paper/2208.01283/) (2022)
- [Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons](https://mlsanity.com/paper/2309.10234/) (2023)
- [URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing](https://mlsanity.com/paper/2311.18352/) (2023)
- [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021)
- [Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications](https://mlsanity.com/paper/2407.06518/) (2024)
- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets](https://mlsanity.com/paper/2406.07996/) (2024)
- [A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications](https://mlsanity.com/paper/1807.07301/) (2018)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11318/) (2024)
- [Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08458/) (2024)
- [QoE-Aware Resource Allocation for Semantic Communication Networks](https://mlsanity.com/paper/2205.14530/) (2022)
- [Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systems](https://mlsanity.com/paper/2401.07224/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving](https://mlsanity.com/paper/2510.18123/) (2025)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking](https://mlsanity.com/paper/2603.06607/) (2026)

## Abstract (arXiv)

Semantic communication transmits the extracted features of information rather than raw data, significantly reducing redundancy, which is crucial for addressing spectrum and energy challenges in 6G networks. In this paper, we introduce semantic communication into a cellular vehicle-to-everything (C-V2X)- based autonomous vehicle platoon system for the first time, aiming to achieve efficient management of communication resources in a dynamic environment. Firstly, we construct a mathematical model for semantic communication in platoon systems, in which the DeepSC model and MU-DeepSC model are used to semantically encode and decode unimodal and multi-modal data, respectively. Then, we propose the quality of experience (QoE) metric based on semantic similarity and semantic rate. Meanwhile, we consider the success rate of semantic information transmission (SRS) metric to ensure the fairness of channel resource allocation. Next, the optimization problem is posed with the aim of maximizing the QoE in vehicle-to-vehicle (V2V) links while improving SRS. To solve this mixed integer nonlinear programming problem (MINLP) and adapt to time-varying channel conditions, the paper proposes a distributed semantic-aware multi-modal resource allocation (SAMRA) algorithm based on multi-agent reinforcement learning (MARL), referred to as SAMRAMARL. The algorithm can dynamically allocate channels and power and determine semantic symbol length based on the contextual importance of the transmitted information, ensuring efficient resource utilization. Finally, extensive simulations have demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE, SRS, and communication delay in C-V2X platooning scenarios.