Unmanned aerial vehicles (UAVs) have demonstrated exceptional capabilities in various applications such as vehicle tracking, military monitoring and communication assistance. With their advantages in mobile communication and integrated multiple sensors, UAVs have great potential to obtain the integration gain through integrated sensing and communications (ISAC) designs. In this paper we focus on the beamforming algorithm for UAV-to-vehicle communications. To reduce the communication overhead of block transmission caused by the high mobility of the target vehicles and the UAV, we utilize the inherent vision functionality of the UAV platform and propose a vision-assisted beamforming framework. Specifically, to obtain the position of to-be-served vehicles, we employ YOLOv5, a deep learning model, for vehicle detection, followed by a Sage-Husa filter for multiple target vehicle tracking. Based on the predicted positions of the vehicles, we propose a lightweight beamforming algorithm to save communication overhead of beam tracking. To validate the effectivess of this work, we perform experiments on the UAVDT dataset for vehicle detection and tracking. The simulation results show that the proposed algorithm achieves significant performance gains on both the effective achievable rates and the energy efficiency.

Existing integrated sensing and communication (ISAC) prototypes mostly operate at millimetre‐wave or THz frequencies, leaving the sub‐6 GHz band less explored. The limited available bandwidth at these frequencies constrains key sensing parameters, such as range resolution, yet this band dominates commercial 5G deployments, making sub‐6 GHz ISAC relevant to existing infrastructure. This letter investigates beam‐sweeping ISAC in the 5G new radio (NR) frequency range 1 (FR1) at 2.4 GHz with 10 MHz bandwidth, where the coarse range resolution ( m) precludes delay‐based sensing. A received signal model is formulated for a bistatic configuration with a directional multi‐active/multi‐passive (MAMP) antenna array at the transmitter, and an SNR‐maximisation‐based scheme is used for target detection. A multi‐receiver beam‐sweeping scheme is then evaluated, where spatially distributed receive antennas are combined with directional transmission for angle‐domain sensing with narrowband signals. The two schemes are validated through over‐the‐air experiments in a single‐input single‐output (SISO) and a single‐input multiple‐output (SIMO) setup, respectively, achieving 19.06 dB sensing and 16.52 dB communication SNR gains with angle accuracy. The results show that angle‐domain beam‐sweeping is a viable sensing strategy for narrowband sub‐6 GHz 5G NR systems.

This study addresses the persistent challenge of balancing interpretability and robustness in black-box deep learning models for automatic modulation recognition (AMR), a critical task in wireless communication systems. To bridge this gap, we propose a novel explainable AI (XAI) framework that integrates symbolic feature interaction concepts into communication signal analysis for the first time. The framework combines a modulation primitive decomposition architecture, which unifies Shapley interaction entropy with signal physics principles, and a dual-branch XAI mechanism (feature extraction + interaction analysis) validated on ResNet-based models. This approach explicitly maps signal periodicity to modulation order in high-dimensional feature spaces while mitigating feature coupling artifacts. Quantitative responsibility attribution metrics are introduced to evaluate component contributions through modular adversarial verification, establishing a certified benchmark for AMR systems. The experimental validation of the RML 2016.10a dataset has demonstrated the effectiveness of the framework. Under the dynamic signal-to-noise ratio condition of the benchmark ResNet with an accuracy of 94.88%, its occlusion sensitivity increased by 30% and stability decreased by 22% compared to the SHAP baseline. The work advances AMR research by systematically resolving the transparency–reliability trade-off, offering both theoretical and practical tools for deploying trustworthy AI in real-world wireless scenarios.