Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications, where a stable communication link is vital for safe flight control and robust data transmission. To develop a reliable UAV communication system, it is necessary to deeply understand the UAV channel characteristics and establish accurate channel models. Channel sounding is the most effective way to obtain realistic channel characteristics and validate the theoretical channel model. However, the studies on UAV channel sounding are still insufficient in terms of system design and data processing due to the complexity of developing a UAV channel sounder. Different from the terrestrial channel sounders, the implementation of a UAV channel sounder is tortured by the limited battery life and payload capacity of the UAV platform. The sounding scheme and data post-processing also need to be specially designed for highly dynamic UAV channels. So far, most existing survey studies on UAV channels focus on modeling methodology and model presentation. To fill this gap, this paper provides a comprehensive survey on the design of the UAV channel sounder, in terms of the hardware scheme, sounding signal, time synchronization, calibration, and data post-processing. Current issues and potential research topics behind existing sounding technologies and measurement campaigns are analyzed. Moreover, future challenges and open issues are also discussed.

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.

For unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) systems, a semantic-based channel state information (CSI) feedback scheme is proposed in this paper. Unlike traditional full CSI feedback, the proposed scheme minimizes feedback burden by utilizing predefined semantic databases at both the transmitter and receiver. Firstly, a deep learning-based clustering method is developed to construct the semantic database from measured CSI samples. Then, an incremental clustering-based identification method is proposed, enabling dynamic updates and adjustments to semantic databases as new CSI is continuously acquired. Finally, the proposed CSI feedback scheme is validated through scenario identification, and extensive channel measurements are conducted in three typical campus scenarios: playground, lake, and buildings. The results show that the accuracy of the semantic feedback-based scenario identification reaches 97.5%, which is 0.6% higher than the accuracy of the full-CSI feedback-based scenario identification. Specifically, the CSI is fed back through semantic database labels, requiring only a few bytes. This significantly reduces feedback burden while maintaining high accuracy of ISAC tasks. Furthermore, the proposed feedback scheme can also be extended to other UAV-assisted applications, such as the Internet of Things and emergency response.

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.

With the rapid development of unmanned aerial vehicles (UAVs), UAV-based communications have shown promising application prospects in beyond-fifth-generation (B5G) and sixth-generation (6G) communication. Air-to-ground (A2G) channel characteristics are significant for UAV-based wireless communications. In this paper, a multi-UAV channel measurement system is developed, which can realize cooperative, accurate, and real-time channel measurements. Measurement campaigns are performed in the campus scenario at the 3.6 GHz frequency band. Based on the measurement data, cross-correlation properties of some typical large-scale channel parameters are extracted and analyzed, including the power delay profile (PDP), path loss (PL), and shadow fading (SF). The analysis results reveal that the cross-correlation of PDP remains larger than 0.6 during the whole measurement, and the decorrelation distance is 14.765 m. The cross-correlation of SF is relatively low, and the decorrelation distance is found to be 4.628 m. These results can provide valuable references for optimizing multi-link UAV communications and node placements.