This paper addresses the impact of asymmetric jitter, which arises from air turbulence or mechanical vibrations, on the three dimensional (3D) attitude angles of aerial intelligent reflecting surface (AIRS) mounted on unmanned aerial vehicle (UAV). To characterize these effects, a physics-based channel model based on the spherical wavefront assumption (SWA) is established. To mitigate the resulting performance degradation, we propose a novel continuous reflection phase based on the planar wavefront assumption (PWA), leveraging the concept of the Zadoff-Chu sequence. This design integrates a conventional reflection phase component with a spatial-frequency-bandwidth-dependent term, effectively broadening the bandwidth of the passive beam. Using the proposed continuous reflection phase, we analyze the normalized array gain function and average received signal power under UAV jitter, deriving approximate expressions for these metrics using Fresnel functions. The analysis demonstrates that the proposed reflection phase can expand the beam bandwidth to cover the potential range of jitter angles. Furthermore, a discrete reflection phase is designed based on the continuous version. Numerical results confirm that the beam bandwidth remains stable as the severity of asymmetric jitter increases, illustrating the effectiveness of the designed phases in mitigating UAV platform instability. Additionally, the results indicate that the proposed reflection phase can effectively compensate for performance degradation caused by UAV jitter.

The integration of movable antenna (MA) technique into unmanned aerial vehicle (UAV) communications offers a promising solution to reliable and energy-efficient non-terrestrial networking for low-altitude economy. To characterize multi-MA-assisted UAV-to-UAV wideband fading channels, we propose a three-dimensional arbitrary-elevation two-concentric-cylinders reference model. Based on this model, we derive the space-time-frequency correlation function (STF-CF) in closed form. From the STF-CF, we also obtain the space-Doppler power spectral density (SD-PSD) and the power space-delay spectrum (PSDS). The excellent agreement between the theoretical PSDS and some previously reported measurement data demonstrates the utility of the reference model. We then establish corresponding simulation models, which produce consistent results with the derived expressions. By leveraging the closed-form correlation function, the gradient of the log-determinant of spatial correlation matrix with respect to the MA positions can be conveniently obtained, which may serve as a method to maximize the ergodic capacity of the multi-MA assisted wideband UAV-to-UAV channels.

Reconfigurable intelligent surfaces (RIS) have attracted significant attention due to their capability of establishing virtual line-of-sight (VLoS) links. This paper proposes a channel model for RIS-assisted millimeter wave (mmWave) communication systems that incorporate the effective aperture (EA) of RIS elements, the horizontal and vertical rotation angles of the RIS, the servomechanism limitations associated with these rotation angles and the activation criteria to constrain the feasible range of these rotation angles. To enhance the system performance, we jointly optimize the horizontal and vertical rotation angles of the RIS with the objective of maximizing the signal-to-noise ratio (SNR) based on the proposed model. An alternating optimization (AO) algorithm is developed to solve this problem efficiently. Specifically, the original optimization problem is decomposed into two subproblems corresponding to independent optimization of the horizontal and vertical angles, and closed-form optimal solutions are derived for each subproblem. Updating iteratively these closed-form solutions yields suboptimal horizontal and vertical rotation angles for the RIS. Moreover, a global optimal solution of closed-form to the original optimization problem is derived for the special case where the base station (BS) is positioned directly in front of the RIS. Numerical results demonstrate that the suboptimal rotation angles obtained by the AO algorithm closely approximate the optimal solutions. Furthermore, the proposed AO algorithm, which jointly optimizes both rotation angles, significantly outperforms the methods that individually optimize either the horizontal or vertical angle.

Reconfigurable intelligent surface (RIS) has the potential to shape the wireless channel into an intelligent programmable wireless propagation environment. RIS-assisted millimeter wave (mmWave) technology is considered as a potential technology for sixth generation (6G) wireless communications. In this article, an RIS-assisted mmWave system is considered, and the corresponding physics-based channel model under the parabolic wavefront assumption, which is a second-order approximation to the spherical wavefront assumption, is established. Based on the parabolic wavefront assumption, the approximate closed-form expression of the direction-dependent Rayleigh distance is derived, which is a supplement to the classical Rayleigh distance. Also, the RIS reflection phase, consisting of a conventional far-field reflection phase and an addition near-field reflection phase, is obtained. The far-field phase compensates the phase variations from the mismatch in the azimuth and elevation angles, and the near-field phase compensates the phase variations caused by the distance differences from the transmitter/receiver to different RIS unit cells. Based on the conventional far-field reflection phase and the designed reflection phase, the received signal power is explored, and the approximate expressions are also obtained by using the Fresnel functions, which are validated by using numerical results. In addition, the numerical results show that the mmWave channel model under parabolic wavefront assumption and the corresponding near-field reflection phases are necessary to explore the RIS-assisted mmWave communication systems.

In the space-air-ground integrated network (SAGIN), high-speed railway (HSR) communication is expected to be enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). However, when both aerial platforms and ground base stations (BSs) provide services to HSR user equipments (UEs), severe signal interference can arise, leading to the system failing to meet the quality of service (QoS) requirements of the flows. In this paper, we introduce the optimization problem of transmission scheduling for STAR-RIS assisted train-to-ground communications in SAGIN. To address this issue, a phase optimization algorithm of passive STAR-RIS is proposed to improve the channel quality of HSR UEs. Furthermore, a coalition game algorithm is proposed to associate HSR UEs with the optimal links that minimize inter-flow interference. Finally, a QoS-aware flow scheduling algorithm is proposed to optimize the order of the flows for the selected links. Simulation results confirm that the proposed scheduling scheme effectively increases the number of completed flows and total transmitted bits for STAR-RIS assisted train-to-ground communications in SAGIN, outperforming traditional methods.

An arbitrary-elevation two-sphere reference model is utilized to mimic the unmanned aerial vehicle (UAV) air-to-air fading channels. The model considers the line-of-sight (LoS), the single-bounced transmit (SBT), the single-bounced receive (SBR), and the double-bounced (DB) rays. Based on this model, the closed-form expression of the space-time correlation function (ST-CF) is obtained for the first time under the widely-used assumption of von Mises-Fisher (vMF) distributed scatterers. To further improve the model’s adaptability to realistic scattering environments, the distribution of the scatterers is generalized from a single unimodal vMF density into a mixture that can possess multimodality. Using the single-vMF ST-CF, the ST-CF under the mixture is also written in closed-form. Corresponding to the reference channel model, both the deterministic and the stochastic simulation models are provided, which yield consistent results with the respective derived expressions. This validates the correctness of the suggested closed-form ST-CFs. Moreover, a detailed analysis of the outage probability and the outage capacity is reported, which offers revealing insights into the behaviors of the system performance with respect to change of some key model parameters under multimodal distributions of the scatterers.

A 3-D arbitrary-elevation two-cylinder reference model is proposed for multiple-input-multiple-output (MIMO) air-to-air communications in unmanned aerial vehicle (UAV) channels. This model accounts for not only the Line-of-Sight (LoS) but also the single-bounced at the transmitter (SBT) and the single-bounced at the receiver (SBR), as well as the double-bounced (DB) rays. Therefore, it is endowed with a high adaptability to various UAV-to-UAV communication scenarios. From the reference model, a closed-form expression of the space-time correlation function (ST-CF) is derived. This expression is shown to be the generalizations of many existing correlation functions from the 2-D one-ring, the 3-D low-elevation one-cylinder, the 2-D two-ring, and the 3-D low-elevation two-cylinder model. Corresponding deterministic and stochastic simulation models are also developed in addition to the reference model. The well agreements between the channel capacities obtained from the simulation models and those from the derived ST-CF not only display the usefulness of the simulators but also confirm the correctness of the derivations. Based on the derived closed-form ST-CF, the effects of some model parameters on the capacity are evaluated in a computationally efficient manner.

Intelligent reflecting surface (IRSs) have recently played a crucial role for numerous application associated with unmanned aerial vehicle (UAV) communications. To provide good quality of service (QoS) for the users, it is necessary to study the IRS-aided UAV communication system with performance analysis. Thus, in this paper, a three-dimensional (3D) geometry-based stochastic multiple-input multiple-output (MIMO) channel model is developed to characterize the IRS-aided air-to-ground (A2G) propagation environments, which takes into account the multipath fading and height-dependent path loss effects. Based on the proposed channel model, the impact of some important system parameters on the outage probability (OP) is numerically investigated. One key insight from our analysis is the AIRS operating on mmWave frequencies is more suitable for high mobility scenarios. The obtained results can provide guidance for achieving better system performance optimization.

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Different from the traditional terrestrial intelligent reflecting surface (IRS), aerial IRS (AIRS) can provide some unique advantages, such as flexible deployment and wider-view signal reflection. In this paper, a three-dimensional (3D) single cylinder simulation channel model is proposed for AIRS-aided multiple-input multiple-output (MIMO) communication systems, where the considered propagation scenario consists of a fixed base station (BS) and a mobile station (MS). Based on the model, the channel impulse response (CIR), spreading function, and channel capacity are derived. Then, some heuristic algorithms are proposed to obtain the phase shifts of the IRS elements. It is found that multipath fading and Doppler effects stemming from the movement of MS can be effectively mitigated via adjusting the tunable phase shifts of the IRS elements. Moreover, the channel capacity of the system could also be improved by the proposed schemes. These findings can be used to lay a foundation for developing intelligent and controllable propagation environments.

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. In this paper, a geometric RIS-assisted multiple-input multiple-output channel model is proposed for fixed-to-mobile (F2M) communications based on a three-dimensional cylinder model. The receiver is regarded as in motion, and the time-varying angles and propagation distances are derived to describe non-stationary channels. Based on the proposed channel model, the effect of RIS on spacetime correlation function is investigated. The results can be used to evaluate and optimize the performance of RIS-assisted F2M communication systems.

Unmanned aerial vehicle (UAV) communications are expected to play a major role in future space-air-ground integrated networks (SAGINs). In this paper, a geometric three-dimensional (3D) non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered. Unlike the existing UAV-based GBSMs, the proposed model considers both local and far clusters in the propagation environments. On this basis, a continuous-time Markov model with two states is used to model the dynamic properties of clusters (i.e., clusters appear/disappear with time), and the closed-form expressions of the survival probabilities of clusters are derived. Furthermore, we derive and investigate some significant statistical properties, including space-time-frequency correlation function, quasi-stationary interval, and Doppler power spectrum. Numerical results show that the local mobile cluster (LMC) component leads to higher time correlation compared with the local stationary cluster (LSC) component. In addition, it is found that the LMC component leads to larger quasi-stationary interval compared with the LSC component. Finally, it is found that the motion of transceivers and clusters, and the changes of carrier frequency introduce significant fluctuations in Doppler power spectrum. These observations and conclusions can be considered as a guidance for mmWave UAV MIMO system design.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) technology can provide high data rate for the fifth generation (5G) and beyond wireless networks. In this paper, a non-stationary geometric mmWave multiple-input multiple-output (MIMO) channel model is proposed for air-to-air (A2A) communications based on a three-dimensional (3D) cylinder model. To describe the real A2A propagation environments, a two-state continuous-time Markov process is introduced to model the dynamic properties (appearance and disappearance) of scatterers. The movements of the transmitter and receiver result in time-varying angles and propagation distances that make the model more general. Based on the proposed model, the space-time-frequency correlation functions are derived using the survival probabilities of scatterers. The effects of some important model parameters on channel correlation and non-stationarity are investigated. The observations and conclusions can be used to evaluate and optimize the performance of mmWave UAV A2A communication systems.

Unmanned aerial vehicle (UAV) communications are envisioned to support numerous applications in the fifth and sixth generations wireless networks. In this paper, a three-dimensional (3D) non-stationary semi-spherical geometrical model is proposed for narrowband multi-input multi-output (MIMO) UAV channels. The Gauss-Markov mobility model is used to characterize the UAV rotation for the first time, which results in time-varying elevation and orientation angles of the antenna array and further leads to channel non-stationarity. Based on the proposed model, the space-time correlation function is derived and investigated in terms of the UAV movements (including pitch, roll, and heave). These observations and conclusions can be used as a reference for the system design and performance analysis of UAV-MIMO communication systems.

Vehicle obstruction is a special scenario in vehicle-to-vehicle (V2V) communications. In this case, channel characteristics including path loss and spatial distributions are obviously different from other typical vehicular communication scenarios. However, the vehicle obstruction scenario is difficult to be identified by global navigation satellite systems (GNSS) or radars, so it is difficult for V2V communication systems to respond to sudden changes in channel characteristics due to vehicle obstructions. Therefore, by correctly identifying the vehicle obstruction scenarios, V2V communication systems can select appropriate propagation channel models to maintain an effective and reliable operating state. For this reason, this paper presents a machine-learning-based vehicle obstruction scenario identification approach for V2V communications. Channel characteristics extracted from measurements form the datasets used to training, then the back-propagation neural network (BPNN) is trained and a scenario identification model is obtained. Subsequently, identification accuracy is verified by using validation data. The results show that the identification accuracy for vehicle obstruction scenarios is more than 97%, which indicates that the approach proposed in this paper shows good performance in vehicle obstruction scenario identification in V2V communications.

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

Unmanned aerial vehicles (UAVs) communications are considered as a promising technology in various areas. In this paper, a three-dimensional (3D) non- stationary geometry-based stochastic model (GBSM) is proposed for UAV air-to- air (A2A) communication environments. The proposed GBSM considers not only both the ground surface and roadside reflections, but also the arbitrary trajectories of both UAV terminals. Based on the proposed model, some important statistical properties such as time- variant time-frequency correlation function and the Doppler power spectrum are derived and analyzed. Finally, numerical results show that a variation of the velocity and moving direction of the UAV has major impacts on the statistical properties of the radio channels, which indicates its usefulness for the performance analysis of UAV communication systems under non- stationary conditions.

For radio propagation, the wireless channel generally changes more significantly in the non-line-of-sight (NLOS) scenario compared to the line-of-sight (LOS) scenario. For vehicle-to-vehicle (V2V) communications, the most typical NLOS scene is large vehicle obstructions. Therefore, in order to understand the radio propagation mechanism and channel characteristics in V2V communication, it is necessary to carry out indepth investigations on V2V channel under the condition of large vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions in the urban environment at 5.9 GHz are carried out. Based on the measured data, extraction and analysis of channel parameters are performed. Specifically, this paper focuses on the directional analysis of V2V channel in the case of large vehicle obstructions. It can be find that the occlusion of large vehicles not only causes additional attenuation of signal energy, but also significantly affects the angular distribution of multipath components in three- dimensional space, and brings larger angular dispersion. These results would be useful for establishing a more accurate channel model and serveing the design of V2V communication system.