Unmanned Aerial Vehicle (UAV)-enabled Orthogonal Time Frequency Space (OTFS)-Integrated Sensing And Communication (ISAC) is a promising technique that simultaneously provides the enhanced Sensing and Communication (S&C) services, while holding significant potential to mitigate the high-Doppler effect. To achieve the advantages of UAV-enabled OTFS-ISAC systems, accurate channel estimation is crucial. However, this process faces a significant challenge of the off-grid phenomenon in the Delay-Doppler (DD) domain, promoting the development of off-grid channel estimation methods. Regrettably, most existing off-grid channel estimation methods mainly focus on optimizing signal modeling, resulting in significantly high computational complexity and leaving substantial room for accuracy improvement. To address these issues, inspired by the benefits of environmental sensing, a sensing-assisted off-grid channel estimation method is proposed in this paper. Specifically, passive UAV radar is employed to sense the location and velocity of ground User Equipment (gUE). Based on Ground-to-Air (G2A) communication link, a radio map is constructed, which captures comprehensive environmental features and signal blockage information. We extract the delay and Doppler priors from this environmental information, which provide crucial insights in DD domain channel from a perspective of propagation environment. Subsequently, by leveraging this prior information, a Sensing-Assisted Grid Calibration-based OGSBI (SAGCSBI) method and a Path Refinement-based Enhanced Channel Estimation (PR-EnCE) method are developed. The proposed methods reduce off-grid errors and mitigate path superposition by integrating environmental information and signal modeling. Simulation results verify the superior performance of the proposed channel estimation methods over state-of-the-art methods and demonstrate the robustness against parameter variations.

In Unmanned Aerial Vehicle (UAV)-assisted millimeter Wave (mmWave) systems, Channel State Information (CSI) feedback is critical for the selection of modulation schemes, resource management, beamforming, etc. However, traditional CSI feedback methods lead to significant feedback overhead and energy consumption of the UAV transmitter, therefore shortening the system operation time. To tackle these issues, inspired by superimposed feedback and Integrated Sensing and Communications (ISAC), a Line of Sight (LoS) sensing-based superimposed CSI feedback scheme is proposed. Specifically, on the UAV transmitter side, the Ground-to-UAV (G2U) CSI is superimposed on the UAV-to-Ground (U2G) data to feed back to the ground Base Station (gBS). At the gBS, the dedicated LoS Sensing Network (LoS-SenNet) is designed to sense the U2G CSI in LoS and NLoS scenarios. With the sensed result of LoS-SenNet, the determined G2U CSI from the initial feature extraction will work as the priori information to guide the subsequent operation. Specifically, for the G2U CSI in NLoS, a CSI Recovery Network (CSI-RecNet) and superimposed interference cancellation are developed to recover the G2U CSI and U2G data. As for the LoS scenario, a dedicated LoS Aid Network (LoS-AidNet) is embedded before the CSI-RecNet and the block of superimposed interference cancellation to highlight the feature of the G2U CSI. Compared with other methods of superimposed CSI feedback, simulation results demonstrate that the proposed feedback scheme effectively improves the recovery accuracy of the G2U CSI and U2G data. Besides, against parameter variations, the proposed feedback scheme presents its robustness.