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This research proposes an optimization framework for X-band UAV systems to balance sensing accuracy and communication rates in vehicular networks. By modeling practical constraints like fading and shadowing, the study offers adaptive time allocation strategies that ensure minimum communication throughput while maintaining reliable sensing. The work provides a theoretical foundation for integrating aerial platforms into intelligent transportation systems without compromising either service domain.
X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks
arXiv:2606.05262v1 Announce Type: cross 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.
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.
Information Theory (cs.IT); Artificial Intelligence (cs.AI)
arXiv:2606.05262 [cs.IT]
(or arXiv:2606.05262v1 [cs.IT] for this version)
https://doi.org/10.48550/arXiv.2606.05262 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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