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 rapid development and broad applications of unmanned aerial vehicle (UAV) based wireless stations in the sky, fundamental understanding and characterization of the realistic air-to-ground (A2G) communication link properties are crucial. In this paper, a UAV-aided channel sounder with a real-time processing hardware system is developed for highly dynamic and non-stationary A2G channel measurements. In the hardware system, a global positioning system (GPS) -based triggering signal is designed, the equivalent antenna pattern affected by UAV airframe is considered, and an appropriate sounding signal is selected, in order to improve the accuracy of measured channel impulse response (CIR). Moreover, real-time hardware processing algorithms for raw channel data, i.e., CIR extraction, system response elimination (SRE), power loss recovery (PLR), and adaptive multi-path component (MPC) recognition are developed and implemented on a single field-programmable gate array (FPGA) chip. In this way, the required storage size of channel data and the processing time for one slice of CIR is greatly decreased, which can meet the requirement of non-stationary A2G channel measurement with a high sampling rate and long-time measurement. A commercial channel emulator is used to reproduce controllable channels and verify the performance of developed channel sounder. Finally, the developed channel sounder is applied to carry out A2G measurement campaigns at 3.5 GHz in a campus scenario. The channel characteristics, i.e., path loss (PL), K-factor, and path angle are analyzed. The measured channel characteristics are consistent with existing measurements under a similar scenario. The estimated path angles are also validated by the theoretical results. Thus, the channel sounder can be used to capture the non-stationary A2G channel characteristics for the system design and algorithm optimization of A2G communications.

In this paper, a new WINNER+ based three dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for multiple-input multiple-output (MIMO) channels is proposed, as well as extended evolving algorithms of time-variant channel parameters. Meanwhile, important statistical properties of the channel model, i.e., time-variant autocorrelation function (ACF), time-variant cross-correlation function (CCF), and timevariant Doppler power spectrum density (DPSD) are derived and analyzed. Moreover, we propose an efficient hardware implementation method, namely sum-of-frequency-modulation (SoFM) method, to generate non-stationary channel coefficients. By utilizing a compact hardware architecture with SoFM modules, the proposed 3D non-stationary GBSM is realized on a field-programmable gate array (FPGA) hardware platform. Simulations and hardware measurement results demonstrate that our proposed channel simulator and emulator can get more accurate and realistic Doppler frequency than those of the existing models. In addition, hardware measurements of statistical properties are also consistent well with the corresponding theoretical ones, which verifies the correctness of both the hardware emulation scheme and theoretical derivations.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role in reliable communications between UAV and ground terminal. This paper proposes a three-dimensional (3D) non-stationary hybrid model including large-scale and small-scale fading for U2G multiple-input-multiple-output (MIMO) channels. Distinctive channel characteristics under U2G scenarios, i.e., 3D trajectory and posture of UAV, fuselage scattering effect (FSE), and posture variation fading (PVF) are incorporated into the proposed model. The channel parameters, i.e., path loss (PL), shadow fading (SF), path delay, and path angle, are generated incorporating machine learning (ML) and ray tracing (RT) techniques to capture the structure-related characteristics. In order to guarantee the physical continuity of channel parameters such as Doppler phase and path power, the time evolution methods of inter- and intra- stationary intervals are proposed. Key statistical properties, including temporal auto-correction function (ACF), power delay profile (PDP), level crossing rate (LCR), average fading duration (AFD), and stationary interval (SI), are analyzed with the impact of the change of fuselage and posture variation. It is demonstrated that both posture variation and fuselage scattering have crucial effects on channel characteristics. The validity and practicability of the proposed model are verified by comparing the simulation results with the measured ones.

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recover the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed. For the given construction accuracy and the priority of each location, the quantity and sampling locations can be jointly optimized. With the sparse sampled data, by mining the sparsity of the spectrum situation and channel propagation characteristics, a SBL-based spectrum data hierarchical recovery algorithm is developed to estimate the missing data of unsampled locations. Finally, the simulated three-dimensional (3D) REM data in the campus scenario are used to verify the proposed methods as well as to compare with the state-of-the-art. We also analyze the recovery performance and the impact of different parameters on the constructed REMs. Numerical results demonstrate that the proposed scheme can ensure the construction accuracy and improve the computational efficiency under the low sampling rate.

The radio environment map (REM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. In view of a limited number of spectrum sensors and constrained sampling time, this paper presents a new three-dimensional (3D) REM construction scheme based on sparse Bayesian learning (SBL). Firstly, we construct a scenario-dependent channel dictionary matrix by considering the propagation characteristic of the interested scenario. To improve sampling efficiency, a maximum mutual information (MMI)-based optimization algorithm is developed for the layout of sampling sensors. Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D REM. We finally use the simulation data of the campus scenario to construct the 3D REMs and compare the proposed method with the state-of-the-art. The recovery performance and the impact of different parameters on the constructed REMs are also analyzed. Numerical results show that the proposed scheme can reduce the required spectrum data and has higher accuracy under the low sampling rate.