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.

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.

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future wireless communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary ray-based channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including inter-path and intra-path are developed and illustrated in detail. The inter-path parameters are calculated in a deterministic way based on the given geometric information. The parameters of intra-path rays are generated in a stochastic way based on the statistical properties, which can be obtained in advance by applying a Gaussian mixture model (GMM) on the massive ray tracing (RT) data of different typical scenarios. Meanwhile, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. To speed up the RT algorithm, the 3D propagation scenario is reconstructed based on the user-defined digital map. Moreover, the theoretical statistical properties of the proposed channel model, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), cumulative distribution function (CDF), and level crossing rate (LCR) are derived. Finally, a typical U2V channel under urban scenario at 28 GHz is generated and validated. Simulation results demonstrate that the PDP and DPSD of the proposed channel model are accorded well with the theoretical ones, and the ACF, CDF, and LCR are consistent with the measured ones as well.

Channel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink channel is firstly formulated as a two dimensional (2D) separable compressive sensing (CS) model according to the sparsity structure of the channel in angle of arrivals (AoAs) and angle of departures (AoDs) domains. Then a separable compressive sampling match pursuit (SCoSaMP) algorithm is proposed to solve the separable CS recovery problem for channel estimation. Based on the separable sparsity structure of the channel, we design the precoding and combining matrices under the metric of mutual information to further improve the performance of channel estimation. Simulations demonstrate the advantages of the proposed algorithm over the traditional CS-based channel estimation methods.

Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing simulation data of UAV mmWave communication links, the proposed parametric channel model is constructed by a line-of-sight path, a ground specular path, and two strongest single-bounce paths. Meanwhile, a new parameter computation method is also developed, which is divided into the deterministic (or geometry-based) part and the random (or empirical) part. The simulated power delay profile and power angle profile demonstrate that the statistical properties of proposed channel model are time-variant with respect to the scattering scenarios, positions and beam direction. Moreover, the simulation results of autocorrelation functions fit well with the theoretical ones as well as the measured ones.

The first task of GPS receiver is to acquire the satellite signal, which tests the all likely 2-D hypotheses of the code phase and the Doppler frequency of the incoming GPS signal. Compressive sensing is a new signal sampling theory, for sparse signal, the signal can be sampled at a lower sampling rate than Nyquist sampling theory. In this paper, a new GPS signal acquisition method is proposed based on compressive sensing. We designed a GPS signal sparse representation dictionary according to the satellite; the position of sparse spike corresponds to the code phase and Doppler frequency. The modified greedy acquisition algorithm that is different from the compressive sensing signal reconstruction, which is used to acquire the GPS satellite. The numerical simulations illustrate that the new method can efficiently acquire the satellite with artificial or real GPS signal. The new method can compress the GPS signal greatly and reduce the transmission energy consumption accordingly, which is hoped to be applied to aircraft or animal monitoring that have little interference noise and need to record GPS data for a long time.