This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Synesthesia of Machines (SoM). To explore the mapping relationship between physical environment and electromagnetic space, we construct a new V2V high-fidelity mixed sensing-communication integration simulation dataset with different vehicular traffic densities (VTDs). Based on the constructed dataset, we develop a novel scatterer recognition (ScaR) algorithm utilizing neural network SegNet to recognize scatterer spatial attributes from LiDAR point clouds via SoM. Subsequently, the developed ScaR algorithm is incorporated into channel modeling to recognize scatterers in a physically informed manner. We further distinguish recognized scatterers into dynamic and static scatterers based on LiDAR point cloud features. The aforementioned procedures determine parameters related to dynamic and static scatterers, e.g., distance, angle, and number, enabling the channel model to generate the corresponding channel impulse responses (CIRs). Through the ScaR algorithm, dynamic and static scatterers change with the variation of LiDAR point clouds over time, which precisely models channel non-stationarity and consistency under different VTDs. Some important channel statistical properties, such as time-frequency correlation function (TF-CF) and Doppler power spectral density (DPSD), are obtained and analyzed. Simulation results match well with ray-tracing (RT)-based results, thus demonstrating the necessity of exploring the mapping relationship and the utility of the proposed model.

In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-uncrewed aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model.

In this paper, a novel adaptive near-field channel model with an extremely large-scale multiple-input multiple-output (XL-MIMO) uniform planar array (UPA) is proposed for sixth generation (6G) multiple-uncrewed aerial vehicle (multi-UAV) cooperative communications. In the proposed model, a novel selective near-field area (SNA) of the XL-MIMO UPA, where the transmission is regarded as spherical wavefront, is proposed to balance complexity and accuracy of near-field channel modeling. To jointly model the non-stationarity on the array, and in the space, time, and frequency domains, an adaptive UPA-UAV-time-frequency non-stationary algorithm is developed, which mimics the non-stationarity on the XL-MIMO UPA for the first time. The channel parameters related to the three-dimensional (3D) continuously arbitrary trajectory and self-rotation of multi-UAVs are also taken into account in the proposed model and the developed algorithm. To explore the channel statistics and validate the proposed model, a new XL-MIMO-UPA-to-multi-UAV channel dataset at low-terahertz (low-THz) frequency band under National Stadium scenario is built. Key UPA-to-multi-UAV channel statistics, such as the array-space-time-frequency correlation function (ASTF-CF), time stationary interval (TSI), Doppler power spectral density (DPSD), and singular value spread (SVS), are obtained. The close agreement between the simulation results and ray-tracing results in National Stadium scenario is achieved, demonstrating the accuracy of proposed channel model.

In this paper, a novel multi-modal uncrewed aerial vehicle (UAV)-to-ground channel model is proposed for sixth-generation (6G) intelligent sensing-communication integration, where communication information and sensing data, i.e., light detection and ranging (LiDAR) point cloud data, are integrated. To thoroughly explore the mapping relationship between the electromagnetic space and the physical environment in UAV-to-ground scenarios, a new intelligent sensing-communication integrated UAV-to-ground dataset with channel information and LiDAR point clouds is constructed in an urban scenario under low, medium, and high vehicular traffic density (VTD) conditions. By detecting dynamic and static objects in the physical environment with LiDAR point clouds, scatterers in the electromagnetic space are divided into dynamic and static scatterers. In addition, the parameters of dynamic and static scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different VTDs in the physical environment. In the proposed model, time non-stationarity and consistency along time axis and the frequency non-stationarity at different frequencies are simultaneously mimicked. The channel statistical properties, such as time-frequency correlation function and Doppler power spectral density (DPSD), are derived. Since simulation results match ray-tracing (RT) results well, the accuracy of the proposed multi-modal UAV-to-ground channel model in urban scenarios is validated.

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the design of intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time. Specifically, by detecting dynamic vehicles and static buildings/trees through LiDAR point clouds via machine learning, scatterers are divided into static and dynamic scatterers. Furthermore, statistical distributions of parameters, e.g., distance, angle, number, and power, related to static and dynamic scatterers are quantified under high, medium, and low VTD conditions. To mimic channel non-stationarity and consistency, based on the quantified statistical distributions, a new visibility region (VR)-based algorithm in consideration of newly generated static/dynamic scatterers is developed. Key channel statistics are derived and simulated. By comparing simulation results and ray-tracing (RT)-based results, the utility of the proposed LA-GBSM is verified.

Eye tracking has shown great promise in many scientific fields and daily applications, ranging from the early detection of mental health disorders to foveated rendering in virtual reality (VR). These applications all call for a robust system for high-frequency near-eye movement sensing and analysis in high precision, which cannot be guaranteed by the existing eye tracking solutions with CCD/CMOS cameras. To bridge the gap, in this paper, we propose Swift-Eye, an offline precise and robust pupil estimation and tracking framework to support high-frequency near-eye movement analysis, especially when the pupil region is partially occluded. Swift-Eye is built upon the emerging event cameras to capture the high-speed movement of eyes in high temporal resolution. Then, a series of bespoke components are designed to generate high-quality near-eye movement video at a high frame rate over kilohertz and deal with the occlusion over the pupil caused by involuntary eye blinks. According to our extensive evaluations on EV-Eye, a large-scale public dataset for eye tracking using event cameras, Swift-Eye shows high robustness against significant occlusion. It can improve the IoU and F1-score of the pupil estimation by 20% and 12.5% respectively, compared with the second-best competing approach, when over 80% of the pupil region is occluded by the eyelid. Lastly, it provides continuous and smooth traces of pupils in extremely high temporal resolution and can support high-frequency eye movement analysis and a number of potential applications, such as mental health diagnosis, behaviour-brain association, etc. The implementation details and source codes can be found at https://github.com/ztysdu/Swift-Eye.

This paper proposes a novel three-dimensional (3D) non-stationary multiple-unmanned aerial vehicle (multi-UAV) cooperative channel model for sixth generation (6G) wireless communication systems. In the proposed multi-UAV cooperative channel model, the channel impulse response (CIR) of sub-channel between each UAV and ground station (GS) is calculated, including line-of-sight (LoS), non-LoS (NLoS), as well as ground reflection transmissions. The relative distance and transmission relationship among sub-channels between multi-UAVs and GS are further modeled and analyzed. To simultaneously capture the cooperative non-stationarity of multi-UAV cooperative channels in space and time domains, a novel cooperation-based space-time (S-T) non-stationary algorithm is developed based on the birth-death (BD) process for the first time. Meanwhile, the UAV-related parameters, e.g., the height and 3D movement of UAVs, are considered. Some important channel statistical properties, such as space-time-frequency correlation function (STF-CF), Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are obtained. Based on the simulation results, multi-UAV cooperative channel statistical properties are analyzed. Finally, the simulation results match well with the ray-tracing results, which validates the accuracy of the proposed multi-UAV channel model.

In this paper, a mixed-bouncing based channel model with cooperative space-array-time (S-A-T) consistency and space-array-time-frequency (S-A-T-F) non-stationarity is proposed for sixth generation (6G) multiple-unmanned aerial vehicle (multi-UAV) cooperative communication systems with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies. To model the transmission propagation in multi-UAV integrated channels more accurately, the single-bouncing transmissions and multi-bouncing transmissions in multi-UAV integrated channels are simultaneously modeled and quantified by a cooperative cluster density index for the first time. Meanwhile, the transmissions through line-of-sight (LoS), ground reflection, single-bouncing, and multi-bouncing are captured. To jointly mimic cooperative S-A-T consistency and S-A-T-F non-stationarity in the integrated scattering environment (SE), a new cooperative consistent and non-stationary modeling algorithm is developed based on the frequency-dependent path gain, visibility region (VR), and birth-death (BD) survival probability. The channel parameters related to multi-UAVs are also taken into account in the developed algorithm. The corresponding multi-UAV cooperative channel statistical properties are derived by taking mixed-bouncing transmission into account. Meanwhile, the accuracy of the mixed-bouncing based multi-UAV integrated channel model with cooperative S-A-T consistency and S-A-T-F non-stationarity is validated as simulation results match well with ray-tracing results.

In this paper, a novel three-dimensional (3D) irregular shaped geometry-based stochastic model (IS-GBSM) is proposed for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channels. To investigate the impact of vehicular traffic density (VTD) on channel statistics, clusters are divided into static clusters and dynamic clusters, which are further distinguished into static/dynamic single/twin-clusters to capture the mixed-bouncing propagation. A new method, which integrates the visibility region and birth-death process methods, is developed to model space-time-frequency (S-T-F) non-stationarity of V2V channels with time-space (T-S) consistency. The continuously arbitrary vehicular movement trajectory (VMT) and soft cluster power handover are modeled to further ensure channel T-S consistency. From the proposed model, key channel statistics are derived. Simulation results show that S-T-F non-stationarity of channels with T-S consistency is modeled and the impacts of VTD and VMT on channel statistics are analyzed. The generality of the proposed model is validated by comparing simulation results and measurement/ray-tracing (RT)-based results.

In this paper, a novel unmanned aerial vehicle (UAV) space-time-frequency (S-T-F) non-stationary channel model with time-space consistency for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. In the proposed model, the line-of-sight (LoS) transmission and non-LoS (NLoS) transmission through ground reflection, single-clusters, and twin-clusters are modeled. Meanwhile, the three-dimensional (3D) continuously arbitrary trajectory and the self-rotation of UAV are imitated. To capture the time-space consistency and S-T-F non-stationarity simultaneously, a new UAV-related non-stationary modeling algorithm that integrates the visibility region (VR), frequency-dependent path gain, and survival probability is developed for the first time. In this algorithm, the UAV-related parameters are considered, including UAV’s height, 3D moving velocity, and self-rotation angles. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the cluster density index influenced by communication scenarios, frequency, UAV’s height, and the distance between transceivers. Some important channel statistical properties, such as S-T-F correlation function (STF-CF), Doppler power spectral density (DPSD), and stationary interval, are derived. Finally, simulation results match well with ray-tracing-based results, which verifies the utility of the proposed model.

In this paper, a novel cooperative array-space-time-frequency (A-S-T-F) non-stationary multi-unmanned aerial vehicle (UAV) channel model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. The channel impulse response (CIR) of the proposed multi-UAV cooperative channel model is derived, including line-of-sight (LoS), ground reflection, and non-LoS (NLoS) transmissions. To capture the 3D continuously arbitrary trajectories of multi-UAVs, the 3D time-varying accelerations of multi-UAVs, clusters, and ground station (GS) and the multi-UAVs’ self-rotations are taken into account. In addition, to model the non-stationarity of massive MIMO mmWave multi-UAV cooperative channels in the A-S-T-F domain, a new cooperative A-S-T-F non-stationary algorithm based on the birth-death (BD) process and the$K$-Means clustering algorithm is developed. Important cooperative channel statistical properties, including cooperative array-space-time-frequency correlation function (ASTF-CF), cooperative Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are derived and investigated. The accuracy of the proposed multi-UAV cooperative channel model is verified by the close agreement between ray-tracing-based results and simulation results.

This paper proposes a novel three-dimensional (3D) mixed-bouncing based unmanned aerial vehicle (UAV) channel model with space-time-frequency (S-T-F) non-stationarity for sixth generation (6G) massive multiple-input-multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems. In the proposed mixed-bouncing based model, the line-of-sight (LoS) transmission, single-bouncing transmission, and multi-bouncing transmission are simultaneously modeled. A new parameter, i.e., cluster density index, is defined and developed. The influence of communication scenarios, frequency, UAV’s height, and distance between transceivers on cluster density index is also analyzed. To capture the S-T-F non-stationarity, a mixed-bouncing based S-T-F non-stationary algorithm is developed based on the birth-death (BD) process and frequency-dependent factor for the first time. In the developed algorithm, the non-stationarity is captured from the perspectives of both single-clusters and twin-clusters. Meanwhile, the impact of UAV-related parameters, such as the UAV’s height and 3D moving velocity, on the non-stationary modeling is considered. Based on the simulation results, the impact of cluster density index on the important channel statistical properties, such as S-T-F correlation function (STF-CF) and Doppler power spectral density (PSD), is explored. Finally, the simulation results match well with the measurement and ray-tracing (RT)-based results, validating the accuracy of the proposed model.

In this paper, based on the geometric stochastic modeling method, a space-time-frequency (S-T-F) non-stationary model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation is proposed for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. It is the first 6G massive MIMO mmWave UAV channel model that considers the 3D continuously arbitrary trajectory of UAV in practice and models S-T-F non-stationarity of 6G UAV channels. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the 3D time-varying accelerations and self-rotations of transceivers and clusters. To further model the S-T-F non-stationarity of UAV channels, a novel UAV-related birth-death (BD) algorithm based on correlated clusters is developed. In the developed algorithm, the impact of typical UAV-related parameters, e.g., the UAV’s moving direction, altitude, and time-varying velocity, on the setting of correlated clusters and the BD process is sufficiently considered. Important channel statistical properties are derived and investigated. Some numerical results and interesting observations are given, which can provide some assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and ray-tracing-based results.

This paper proposes a non-stationary three-dimensional (3D) irregular-shaped geometry-based stochastic model (IS-GBSM) for fifth generation (5G) and beyond massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. This is the first sixth generation (6G) massive MIMO mmWave UAV IS-GBSM that can model the UAV channel space-time non-stationarity, and can describe the impact of some unique UAV-related parameters, e.g., the UAV’s moving direction, height, and speed, on channel statistical properties. To better represent the space-time non-stationarity in UAV scenarios, a novel UAV-related space-time cluster evolution algorithm is developed. The developed algorithm considers the characteristics of UAV communications on the modeling of space-time non-stationarity. Based on the proposed model, some channel statistical properties are derived and thoroughly investigated, including the space-time-frequency correlation function, Doppler power spectral density, envelope level crossing rate, and average fade duration. Some numerical results and interesting observations are given, and the impact of UAV-related parameters on channel statistical properties is explored, which can provide assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the applicability of the proposed model is verified by the close agreement between simulation results and measurement.

This article proposes a novel 3-D nonstationary wideband vehicle-to-vehicle (V2V) geometry-based stochastic model (GBSM) with uniform planar antenna arrays (UPAs) for massive multiple-input–multiple-output (MIMO) wireless communication systems. In the proposed GBSM, a novel method, so-called birth–death (BD) process and seed algorithm-based selective cluster evolution, is developed to capture the space nonstationarity of V2V massive MIMO with UPA channels. The time nonstationarity is further mimicked by employing this novel method over the entire timeline. In addition, the proposed GBSM not only models the reflection of the ground, but also divides clusters into static clusters and dynamic clusters to sufficiently investigate the impact of vehicular traffic density (VTD) on channel statics. The channel parameters are properly calculated by 3-D vectors, resulting in the proposed GBSM with high accuracy and low complexity. Important statistical properties, such as the space-time correlation function (S-T CF), spatial cross-correlation function (CCF), temporal auto-correlation function (ACF), and Doppler power spectrum density (PSD) are derived and thoroughly investigated. Simulation results show that the space-time nonstationarity is successfully mimicked and the VTD has a significant impact on channel statistics. Finally, an excellent agreement is achieved between simulation results and measurements, validating the accuracy of the proposed GBSM.

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

This paper proposes a procedure of predicting channel characteristics based on a well‐known machine learning (ML) algorithm and convolutional neural network (CNN), for three‐dimensional (3D) millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above‐mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

This paper first proposes a three dimensional (3-D) non-stationary wideband multi-confocal ellipsoid channel model with uniform planar antenna array (UPA) for massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed 3-D geometry-based stochastic model (GBSM) not only considers the non-stationary channel characteristics in time domain but also describes the non-stationary channel characteristics in array domain by adopting a birth-death (BD) process and seed algorithm on UPA for the first time. At the meanwhile, selective cluster evolution and cluster evolution areas (CEAs) are first proposed in this paper. Only clusters in CEAs go through cluster evolution because near-field effect and other clusters can be observed by all antennas. Channel parameters, including delay, Doppler frequency, angle of departure (AoD), and angle of arrive (AoA), are considered in both the azimuth direction and elevation direction. This paper also considers rotations of UPAs. Based on the proposed theoretical reference model, the relevant simulation model is also obtained. The statistical properties of theoretical reference model and simulation model can match well with numerical results.

This paper first proposes a novel three dimensional (3-D) wideband multi-confocal ellipsoid model for wireless multiple-input multiple-output (MIMO) communication channels. The proposed 3-D geometry-based stochastic model (GBSM) describes the channel in both the azimuth direction and elevation direction, including delay, Doppler frequency, angle of departure (AoD), and angle of arrive (AoA). It can be shown that the ellipsoid model can capture the effect of the clusters more accurately than the previous elliptic-cylinder model. Using the proposed theoretical model as the reference model, the corresponding simulation model is also derived. Numerical results demonstrate that their statistical properties can match well.