To achieve space-air-ground-sea integrated communication networks for future sixth generation (6G) communications, unmanned aerial vehicle (UAV) communications applying to maritime scenarios serving as mobile base stations have recently attracted more attentions. The UAV-to-ship channel modeling is the fundamental for the system design, testing, and performance evaluation of UAV communication systems in maritime scenarios. In this paper, a novel non-stationary multi-mobility UAV-to-ship channel model is proposed, consisting of three kinds of components, i.e., the line-of-sight (LoS) component, the single-bounce (SB) components resulting from the fluctuation of sea water, and multi-bounce (MB) components introduced by the waveguide effect over the sea surface. In the proposed model, the UAV as the transmitter (Tx), the ship as the receiver (Rx), and the clusters between the Tx and Rx, can be seen as moving with arbitrary velocities and arbitrary directions. Then, some typical statistical properties of the proposed UAV-to-ship channel model, including the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectrum density (PSD), delay PSD, angular PSD, stationary interval, and root mean square (RMS) delay spread, are derived and investigated. Finally, by comparing with the available measurement data, the accuracy of proposed channel model is validated.

A commonly used assumption in the existing unmanned aerial vehicle (UAV) channel models is that the UAV flies along linear trajectories. However, this assumption does not always hold in realistic UAV communication scenarios. In this paper, we relax the linear trajectory restriction and present a temporally non-stationary channel model for UAV-to-ground communication scenario. The temporal non-stationarity of the channel stemming from time-varying heading directions of the UAV is properly modeled. This is achieved by combining a geometry-based stochastic model (GBSM) and an aeronautic random mobility model (RMM), which is introduced to characterize the movement pattern of UAVs. Based on UAV and ground station (GS) trajectories, time-varying model parameters and statistics including space-time-frequency correlation function (STF-CF), Doppler power spectrum density (PSD), delay PSD, and stationary interval are derived. The influences of the RMM-related parameters on the statistics are presented and analyzed. Some of those statistics are verified by measurements, showing the practicability of the model. The work presented in this paper is helpful in designing UAV communication system considering realistic UAV trajectories.

This paper focuses on securing confidential communication in multiple intelligent reflecting surfaces (IRS) assisted terahertz (THz) systems, where a potential eavesdropper can intercept either the base station (BS)-IRS link or the IRS-user link. Notably, the secure transmission may be intercepted and blocked by the eavesdropper due to the blockage-prone nature in THz bands. To characterize the blocking effects of the eavesdropper, the blocking-based path loss is first investigated. With the imperfect eavesdropper channel state information (ECSI), the worst-case secrecy rate (WCSR) is derived, and a joint optimization problem of hybrid beamforming at the BS and reflecting beamforming at the IRS is formulated. For the BS-IRS link eavesdropping, the zero-forcing (ZF) principle-based hybrid beamforming and the closed-form phase shifts of multiple IRSs are respectively proposed. For the IRS-user link eavesdropping, an iterative algorithm is proposed to tackle the non-convex optimization problem with a given information leakage threshold. Finally, a robust secure transmission strategy for multi-eavesdropper systems is further investigated. Simulation results demonstrate that compared with blockage-unaware scenarios, our proposed scheme can resist the adverse effects of the blockage-prone nature of THz waves on information security, and significantly boost secrecy performance.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.

Massive multiple-input multiple-output (MIMO) channels are distinctly characterized by their array non-stationarity, which has not been considered in the existing beam domain channel models (BDCMs). In this paper, the array non-stationarity of massive MIMO channels is modeled by the spatially consistent visibility regions (VRs) over a large uniform planar array (UPA) in terms of individual multipath components (MPCs). Based on this, a novel three-dimensional (3D) BDCM incorporating the effects of array non-stationarity is proposed. Statistical properties of the proposed BDCM including channel power, power leakage, space-time-frequency correlation function (STF-CF), and beam spread are derived. The ergodic and outage capacities are evaluated. The impacts of array non-stationarity on those statistics and channel capacity are analyzed. Results suggest that the beamwidths or spatial resolutions of the BDCM for different directions are not equal due to the array non-stationarity. This in turn increases the power leakage and correlation between channel elements and reduces the beam domain channel capacity.

Towards the sixth-generation (6G) wireless communication, unmanned aerial vehicles (UAVs) have been regarded as an indispensable part due to its flexible deployment, wide coverage, and high mobility. This also creates challenges for channel research. Scatterers are normally present in the structure of clusters during UAV communication, and cluster-based channel modeling is significant. In this paper, the variational BayesianGaussian mixture model (VB-GMM) algorithm is proposed for clustering, which takes into account the time-space properties. Cluster tracking is implemented using the multipath component distance (MCD) algorithm. Intra- and inter-cluster characterization, such as the number of clusters, cluster power distribution, angular/delay offset, and angular/delay spreads, are well studied. Moreover, cluster lifetime and birth-death (B-D) properties are extracted and analyzed. Based on these cluster characteristics acquired by machine learning (ML) method, a novel UAV-toground communication channel model is proposed, and a fourstate Markov chain is also introduced to portray the evolution of clusters. Simulation results match well with channel measurements, which verifies the practicality of the proposed model. This paper can give theoretical and technical support for the design and evaluation of UAV-to-ground communication systems