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

arXiv: https://arxiv.org/abs/2606.05262 · HTML: https://mlsanity.com/paper/2606.05262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication](https://mlsanity.com/paper/2203.06358/) (2022)
- [Radar and Communication Co-existence: an Overview](https://mlsanity.com/paper/1902.08676/) (2019)
- [Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation](https://mlsanity.com/paper/2202.09969/) (2022)
- [ISAC from the Sky: UAV Trajectory Design for Joint Communication and Target Localization](https://mlsanity.com/paper/2207.02904/) (2022)
- [The ITU Vision and Framework for 6G: Scenarios, Capabilities and Enablers](https://mlsanity.com/paper/2305.13887/) (2023)
- [UAV-to-Ground Communications: Channel Modeling and UAV Selection](https://mlsanity.com/paper/2004.13618/) (2020)
- [Sharing Spectrum and Services in the 7-24 GHz Upper Midband](https://mlsanity.com/paper/2402.08649/) (2024)
- [Energy-Aware Resource Allocation and Trajectory Design for UAV-Enabled ISAC](https://mlsanity.com/paper/2302.10124/) (2023)

## Abstract (arXiv)

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.