Drones are expected to be promising aerial platforms in air-to-ground (A2G) integrated communication networks, where the A2G propagation channel is fundamental for reliable communication links. This paper proposes a hybrid parameter generation framework combining the deterministic and statistical methods for a cluster-based A2G channel model. In this framework, the map-based deterministic method is used to generate delay and angle parameters, which can achieve great scenario consistency. However, it is difficult for users to provide precise material information of scatterers, which would cause deviation of power parameters. To tackle this issue, a measurement-driven power generation method is proposed. Firstly, a bandwidth-dependent clustering method is developed to group the rays into clusters. Then, the cluster power is generated by measurement-driven statistical models with a power-decomposition idea. It decomposes the power parameter into several parts that are less dependent on the scenario. Moreover, it can avoid massive measurement campaigns and is more robust when applied in unmeasured scenarios. Finally, a new channel measurement campaign in a street canyon scenario is performed for validations. The proposed method is also compared with a ray-tracing (RT) method and the 3rd Generation Partnership Project (3GPP) channel model. It is shown that the proposed framework and power generation method are great alternatives for accurate and robust modeling requirements under specific A2G communication scenarios.

This paper proposes an early detection method of the pole blockage for millimeter-wave (mmWave) railway communications. In this method, the (line-of-sight) LoS cluster power gain is used for early detection instead of the total received power to reduce the effect of the other scatterers in dynamic railway scenarios. Moreover, the power difference calculation and triggering value searching steps are introduced to handle issues such as time-varying mean power gain, accidental power losses, and so on. Simulation and measurement cases are performed in railway communication scenarios at 60 GHz for validation. The results show that the proposed method can early detect the blockage area before a significant power attenuation occurs. The performance of the proposed detection method is also compared with that of the existing method.

Unmanned aerial vehicle (UAV) communication is a promising part of next generation mobile communication network. Path loss (PL) is vital for the communication quality, where vegetation penetration loss (VPL) is a critical factor under UAV-to-ground (U2G) scenarios. In this paper, we propose a VPL model considering the foliage density and moisture content, and a measurement system is developed and implemented to acquire a large amount of channel data for fitting model parameters. Measurement results show that the proposed VPL model is more accurate and universal compared with typical models such as COST-235 and FITU-R. The proposed model and measurement results can provide a valuable reference for U2G communication in vegetation scenarios.

Unmanned aerial vehicle (UAVs) are a key platform in the sixth generation (6G) communication networks, where integrated sensing and communication (ISAC) is also a promising technology that requires real-time channel estimation. This paper proposes a robust and efficient angle-of-arrival (AOA) estimation method based on a field-trained neural network (NN) for low-latency UAV ISAC applications. In this method, the NN is pre-trained quickly in the field with each receiving antenna element's channel state information (CSI) and the transceivers' locations. We extract the channel multi-paths from the CSI and calculate the path phases as the training data set in real time. Then the pre-trained NN is used for high-efficient AoA estimation in real time. A real-time UAV channel sounder is utilized to verify the proposed method. The measurement results show that the proposed field-trained estimation method is faster and more robust compared with the traditional method and fixed-trained NN. The proposed angle estimation method is valuable for UAV channel estimation and low-latency UAV ISAC applications.

In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.

Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3-D) scattering space, 3-D velocity, 3-D antenna array, and especially 3-D rotations, a machine learning (ML)-integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, an ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the backpropagation-based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF), are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3-D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.