Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

• URL: http://arxiv.org/abs/2512.09621v1
• Date: Wed, 10 Dec 2025 13:08:05 GMT
• Title: Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks
• Authors: Jingbo Zhang, Maoxin Ji, Qiong Wu, Pingyi Fan, Kezhi Wang, Wen Chen,
• Abstract summary: This paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables Vehicle Users (VUs) to perform semantic task offloading.<n>Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Programming (MINLP) problem.<n> Simulations show that performance of this scheme is superior to that of other algorithms.
• Score: 23.818354646156823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway scenarios, this paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables Vehicle Users (VUs) to perform semantic task offloading via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Nonlinear Programming (MINLP) problem, which is transformed into two subproblems. First, we innovatively propose a multi-agent proximal policy optimization task offloading optimization method based on parametric distribution noise (MAPPO-PDN) to solve the optimization problem of the number of semantic symbols; second, linear programming (LP) is used to solve offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.

Related papers

• Dependency-Aware Task Offloading in Multi-UAV Assisted Collaborative Mobile Edge Computing [53.88774113545582]
  This paper presents a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework.<n>It aims to minimize the system cost, and thus realize an improved trade-off between task consumption and energy consumption.<n>We show that the proposed scheme can significantly reduce the system cost, and thus realize an improved trade-off between task consumption and energy consumption.
  arXiv  Detail & Related papers  (2025-10-23T02:55:40Z)
• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning [11.695622067301128]
  We propose a dual-layer UAV-assisted edge computing architecture based on partial offloading.<n>The proposed architecture enables efficient integration and coordination of heterogeneous resources.<n>We show that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-07-08T07:10:52Z)
• Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics [4.734135226897704]
  vehicular ad hoc networks (VANETs) deal with a set of communicating vehicles which are able to spontaneously interconnect.<n>It is crucial to make an optimal configuration of the communication protocols previously to the final network deployment.
  arXiv  Detail & Related papers  (2025-01-15T14:59:00Z)
• Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning [14.942677904783759]
  We formulate an optimization problem to optimize the VFL training performance, considering the energy constraints and mobility of vehicles.<n>We propose a V2V-enhanced dynamic scheduling algorithm to solve it.<n>We show that the proposed algorithm enhances the image classification accuracy on the CIFAR-10 dataset by 4.20% and reduces the average displacement errors on the Argoverse trajectory prediction dataset by 9.82%.
  arXiv  Detail & Related papers  (2024-06-25T11:15:53Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach [8.168647937560504]
  This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an computation-enabled MEC system. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. We propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where we design a framework enabling interaction between two agents.
  arXiv  Detail & Related papers  (2022-05-20T02:00:53Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication [53.47785498477648]
  This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem. The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
  arXiv  Detail & Related papers  (2020-01-22T08:51:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.