X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks

• URL: http://arxiv.org/abs/2606.05262v1
• Date: Wed, 03 Jun 2026 16:18:18 GMT
• Title: X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks
• Abstract summary: Uncrewed aerial vehicles (UAVs) are increasingly considered as aerial platforms capable of providing both sensing and communication services.<n>This paper investigates the optimal time allocation for a UAV-enabled integrated sensing and communication (ISaC) system operating in the X-band for vehicular networks.
• Score: 7.031234391152914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncrewed aerial vehicles (UAVs) are increasingly considered as aerial platforms capable of providing both sensing and communication services, representing a promising paradigm for intelligent transportation systems. This paper investigates the optimal time allocation for a UAV-enabled integrated sensing and communication (ISaC) system operating in the X-band for vehicular networks. We analyze the trade-off between sensing accuracy and communication performance under practical UAV constraints and fading effects, considering both single-shadowing and double-shadowing channel models. An optimization framework is developed to allocate time between sensing and communication while guaranteeing minimum communication rates and sufficient sensing reliability. Simulation results demonstrate adaptive time allocation strategies, highlighting how UAV-to-ground channel conditions and target distances influence the balance between sensing and communication in smart mobility scenarios.

Related papers

• CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System [10.721757094847812]
  This paper describes an unmanned aerial vehicle (UAV)-assisted integrated sensing communication (ISAC) system.<n>The proposed ISAC system is designed to ensure reliable communication towards a downlink user.
  arXiv  Detail & Related papers  (2026-07-21T22:25:03Z)
• A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles [51.449502491030756]
  We present a multi-task semantic communication framework for satellite-assisted autonomous driving.<n>The framework is trained end-to-end to jointly optimize both tasks.<n>Results show that the proposed approach achieves significant bandwidth reduction of up to 87.23% to 98.17%.
  arXiv  Detail & Related papers  (2026-07-15T06:49:41Z)
• Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications [73.26813382994867]
  We propose a delay-tolerant multi-agent deep reinforcement learning algorithm that integrates a delay-penalized reward to encourage information sharing among UAVs.<n>We show that our new approach achieves over 50% reduction in information delay and 75% throughput gain compared to the conventional MADRL.<n>It is also effective in reducing the need for UAVs' information exchange and thus fostering practical deployment of MADRL in UAV-assisted wireless networks.
  arXiv  Detail & Related papers  (2026-03-23T05:42:50Z)
• SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks [26.85167428129155]
  This paper presents a trajectory prediction framework that integrates semantic communication with Agentic AI to enhance predictive performance in vehicular environments.<n>In vehicle-to-infrastructure (V2I) communication, a feature-extraction agent at the Roadside Unit (RSU) derives compact representations from historical vehicle trajectories, followed by semantic reasoning performed by a semantic-analysis agent.<n>The RSU then transmits both feature representations and semantic insights to the target vehicle via semantic communication, enabling the vehicle to predict future trajectories by combining received semantics with its own historical data.
  arXiv  Detail & Related papers  (2025-11-30T11:06:58Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• 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)
• Age of Information Minimization in UAV-Enabled Integrated Sensing and Communication Systems [34.92822911897626]
  Unmanned aerial vehicles (UAVs) equipped with integrated sensing and communication (ISAC) capabilities are envisioned to play a pivotal role in future wireless networks.<n>We propose Age Information (AoI) system that simultaneously performs target sensing and multi-user communication.
  arXiv  Detail & Related papers  (2025-07-18T18:17:09Z)
• Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates [2.607046313483251]
  Millimeter wave (mmWave)-based frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission.<n>In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing.
  arXiv  Detail & Related papers  (2024-12-24T15:32:58Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies [14.518558523319518]
  Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs) It is critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions. We develop a reinforcement learning framework to characterize efficient network selection and autonomous driving policies.
  arXiv  Detail & Related papers  (2022-08-03T04:33:02Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)

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.