Channel models with a good balance of pervasiveness, accuracy, and efficiency are important for the design and optimization of the sixth generation (6G) wireless communication systems. In this paper, a pervasive beam domain channel model (BDCM) capable of modeling all frequency bands and scenarios in 6G is proposed. Unlike traditional geometry-based stochastic models (GBSMs) that describe channels between antenna pairs in the space domain, the pervasive BDCM reformulates the channel in terms of beam pairs to describe special channel characteristics in the beam domain, such as sparsity and Doppler insensibility. The proposed BDCM incorporates essential spatial wideband and spherical wavefront effects for ultra-massive multiple-input multiple-output (MIMO) by considering the nonlinear phase variations across antenna arrays. The pervasive transform matrices for different antenna configurations are derived to enable flexible conversions between the pervasive GBSM and pervasive BDCM. In addition, key statistical properties of the BDCM are derived and analyzed. The proposed pervasive BDCM in different frequency bands and scenarios are validated by measurement data and compared with the GBSM results. The complexity analysis reveals that the proposed pervasive BDCM significantly reduces the computational complexity compared with the pervasive GBSM under different scatterer densities.

Massive multiple-input multiple-output (MIMO) technologies have been greatly developed for the sixth-generation (6G) communication systems. Based on full-digital antenna arrays, asymmetric massive MIMO communication systems can provide distinct beamforming patterns for uplink and downlink chains. In this paper, a terahertz (THz) channel model for asymmetric massive MIMO communication systems considering the influence of different beam patterns is proposed. The proposed model incorporates channel characteristics of massive MIMO based on spherical-wavefront modeling and distance-dependent steering vectors. The asymmetry between uplink and downlink is considered through antenna beam patterns and array configurations. The statistical properties of the channel model are derived. These channel characteristics and system performance, such as channel capacity, are simulated and compared under different beam patterns. The results show that the space-time-frequency (STF) correlations increase while the delay spread and angular spread decrease when the beam patterns are more concentrated. It is also found that the channel capacity can be increased by utilizing high-gain and narrow beam patterns.

Millimeter-wave (mmWave) communications have a wide range of applications in indoor scenarios. However, the mmWave channel characteristics such as high path loss (PL), blockage effect, and temporal non-stationarity for indoor multiscenarios are not fully studied by using the same channel sounder, especially in the moving conditions. In this paper, indoor mmWave channel measurements are conducted at 28 GHz band in an office lounge and a lobby, considering both stationary and moving states of transmitter. Channel characteristics including PL, delay power spectral density (PSD), angular PSD, root mean square (RMS) delay spread (DS), and RMS angular spread (AS) for different scenarios are compared and analyzed. The relationships between channel characteristics and environments are thoroughly investigated. Measurement results confirm the clusters birth-death process and the channel temporal nonstationary properties. Multipath components (MPCs) caused by multiple-order reflections from walls are observed to show a significant impact on channel characteristics in the lobby.

Reconfigurable intelligent surfaces (RISs) are considered as one of the potential technologies for the sixth generation (6 G) wireless communication systems. Research on RIS channels is crucial for future RIS-assisted communication systems. However, current RIS channel measurements are only conducted when the receiver (Rx) is stationary, lacking moving channel measurements. In this paper, RIS is used as the transmitter (Tx) antenna in a hall environment to measure the 28 GHz RIS channel. We use near-field coding and far-field coding to measure the RIS channel during the movement process, respectively. The channel characteristics, including path loss, delay spread (DS), Ricean K-factor (KF), temporal autocorrelation function (TACF), and channel capacity are analyzed. It is found that in the hall environment, the coding mode of RIS exerted significant influences on path loss and channel capacity, whereas their impacts on DS, KF, and TACF were relatively minor.

Due to the agile three-dimensional (3D) mobility and flexibility, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in sixth-generation (6G) wireless networks. In UAV communication systems, 3D beamforming is an effective technique for performance enhancement. To take full advantage of 3D beamforming in UAV communication systems, we need to accurately characterize and efficiently predict highly dynamic UAV channels in the beam domain. This paper first proposes a novel beam domain channel model (BDCM) considering random UAV trajectories and fuselage vibrations. Then, we propose a beam domain channel tracking algorithm to capture variations of multipath component (MPC) parameters in UAV channels. Finally, we put forward a novel beam domain channel prediction scheme for UAV communication systems utilizing channel sparsity and high temporal correlation in the beam domain. The proposed channel prediction scheme can extract channel variation trends utilizing the echo state network (ESN) and predict channel parameters in subsequent time blocks based on the history channel information. Simulation results show that the proposed channel prediction scheme outperforms the conventional prediction scheme based on angular speed estimation in terms of both prediction accuracy and communication system performance.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) and a beam domain channel model (BDCM) for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems are proposed. The spherical wavefront and space-time-frequency non-stationarity introduced by massive MIMO, movements of the user and clusters, and large bandwidth of mmWave communications are incorporated. The shared clusters between the sensing channel and communication channel caused by the scattering characteristics are also considered. Based on the proposed channel model, important statistical properties are derived and simulated, including the space-time-frequency correlation function (STF-CF), root mean square (RMS) beam spread, RMS delay spread, RMS Doppler spread, coherence time, and channel capacity. By comparing the statistical properties of the sensing channel and the communication channel, it is found that the sensing channel exhibits more significant temporal non-stationarity. Moreover, the distribution of clusters for the sensing channel and communication channel shows significant difference, which is confirmed by the simulation results of RMS beam spread and RMS delay spread.

The sixth generation (6G) wireless communication envisions global coverage, all spectra, and full applications, which correspondingly creates many new communication scenarios. As the foundation of 6G communication system design, network planning, and optimization, more intelligent scenario identification algorithms are necessitated in wireless channel modeling to automatically match suitable parameters for various scenarios. With channel statistics and the efficient channel attention (ECA) mechanism, we propose an improved residual network (ResNet) to identify scenarios in the 6G space–air–ground–sea framework. Datasets from both channel measurements and 6G pervasive channel model (6GPCM) simulations are collected to establish a scenario channel characteristic database, including the numbered scenarios and channel statistical properties such as root mean square (RMS) delay spread (DS), RMS angle spread (AS), and stationary distance/time/bandwidth, etc. During the training and verification process, the proposed algorithm is optimized for 29 scenarios, and the identification accuracy of the proposed ECA–ResNet is higher than the convolutional neural network (CNN) and recurrent neural network (RNN). Finally, the cumulative distribution functions (CDFs) of RMS AS and RMS DS for interoffice main road, office outdoor, office, and industrial Internet of Things (IIoT) scenarios are verified according to the measurement data.

In this paper, a novel beam domain channel model (BDCM) for sixth generation (6G) ultra-massive multiple-input multiple-output (MIMO) wireless communication systems is proposed by transforming from an existing geometry-based stochastic model (GBSM). Both the GBSM and BDCM consider the special channel characteristics of ultra-massive MIMO, including spherical wavefront and spatial non-stationarity properties. Steering vectors of the spherical wavefront are derived by higher-order Taylor expansion. By sampling the angle and distance rings, steering matrices of the spherical wavefront can be transformed to unitary matrices. This helps to achieve a perfect transformation from the GBSM to the BDCM in the near-field condition. Meanwhile, it opens up a view of beam domain for channel characteristic analysis to reduce the model complexity. Common statistical properties of the GBSM and BDCM in the near-field and simplified far-field steering vector conditions are studied, including spatial cross-correlation functions (SCCFs), temporal autocorrelation functions (TACFs), frequency correlation functions (FCFs), etc. Specific statistical properties, such as the root mean square (RMS) angular spread of the GBSM and RMS beam spread of the BDCM, are studied. Channel capacities of the GBSM and BDCM are investigated and compared with measurement data. It turns out that simulated capacities considering near-field steering vector conditions show good agreements with measured capacities. On the other hand, simulated capacities considering far-field steering vector conditions show a large discrepancy with measured capacities. This indicates that near-field effects need to be included in the channel modeling and performance evaluation of 6G ultra-massive MIMO communication systems.

In this paper, a novel ultra-massive multiple-input multiple-output (MIMO) ship-to-ship beam domain channel model (BDCM) for maritime communications is proposed, which is transformed from a geometry-based stochastic model (GBSM). The location-dependent property and sparse user and scattering distribution property for maritime communication channel are considered. In addition, the scattering effect of the sea surface and the limited range of angle for the evaporation duct propagation are modeled by the scattering masking function and angle filtering function, respectively. Important statistical properties are studied, including the spatial cross-correlation function (SCCF) and temporal autocorrelation function (TACF), etc. The channel capacity is studied. Simulation results show that the ship speed, wind speed, and antenna configuration have great influence on channel characteristics and system performance. In addition, the simulation result of the root mean square (RMS) delay spread agrees with the measurement data, which proves the correctness of the model.

In this paper, we first propose a 3-dimensional (3-D) non-stationary geometry-based stochastic model (GBSM) for maritime massive multiple-input multiple-output (MIMO) communication systems with the uniform circular array (UCA) configuration. To reduce the model complexity and improve the mathematical tractability, a novel beam domain channel model (BDCM) is then proposed based on the transformation of the corresponding GBSM from the array domain to the beam domain for maritime communications. In the proposed BDCM, the beamforming matrices suitable for UCA structures are constructed and their invertibility is demonstrated to ensure the practicability of the BDCM. Two methods are used to characterize the array non-stationarity in maritime massive MIMO channels. First, the evolution of clusters over the large UCA is modeled by the visibility regions (VRs) attached to individual multipath components (MPCs). Second, the sphere wavefront (SWF) effect is captured by dividing the UCAs into several sub-arrays. Based on the proposed GBSM and BDCM, some important channel statistical properties are studied and compared, including channel power, power leakage, space-time-frequency correlation function (STF-CF), and root-mean-square (RMS) Doppler/beam spreads. Also, the importance of considering the array non-stationarity in maritime communication channels is revealed.

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

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.

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.

In the sixth generation (6G) wireless communication systems, the employment of ultra-massive multiple-input multiple-output (MIMO) technology can provide significant performance improvement. Meanwhile, it introduces some new channel propagation characteristics. For 6G system design and evaluation, a comprehensive ultra-massive MIMO channel characteristic analysis and an accurate channel modeling methodology are indispensable. In this paper, we emphasize on the joint correlation property analysis between the transmitter (Tx) and receiver (Rx) sides of ultra-massive MIMO channels. Firstly, the Weichselberger model generated based on the mutual coupling matrix of Tx and Rx eigenvectors is introduced and a geometrical based stochastic model (GBSM) is presented. Secondly, an urban ultra-massive MIMO channel measurement campaign at 5.3 GHz is conducted. By splitting the ultramassive array into several subarrays, the variances of mutual coupling matrices and corresponding power angular spectrums (PASs) along different subarrays are observed. They show great consistency and exhibit evident spatial non-stationarity. Lastly, channel capacities simulated by the Weichselberger model and twin cluster GBSM are compared with that calculated using the sub-channel measurement data. Through the joint optimization of mutual coupling matrix and capacity, GBSM can provide better fitness of real channel measurement data. We also verify that new channel characteristics at Tx/Rx side and between both sides should all be included in future ultra-massive MIMO channel modeling.

Beamforming can flexibly change the beamwidth and beam direction to achieve the best system performance in the sixth generation (6G) mobile communication. The asymmetric communication systems based on all-digital beamforming have huge advantages, such as more user access in uplink and better signal-to-noise ratio (SNR) in downlink. For asymmetric communication systems, accurate channel models are especially important. In this paper, we apply the space-time-frequency non-stationary channel model to the asymmetric channels by generating correlated parameters in the uplink and downlink with different antenna patterns. Then we investigate the scattering distributions and statistical properties with different antenna array patterns. The simulation results indicate that the scattering distributions and correlation functions are greatly affected by antenna patterns.

As a promising key technology for the sixth generation (6G) wireless communication systems, the reconfigurable intelligent surface (RIS) is able to intelligently control the propagation environments. In order to investigate the properties of RIS-assisted channels, related channel measurements are necessary. However, the channel parameter estimation (CPE) work in RIS-assisted channel measurements is challenging since the RIS can only tune part of the channel. In this paper, we present a novel channel sounding procedure firstly where channels are observed through different RIS transmission modes. Then a space-alternating generalized expectation-maximization (SAGE) based CPE scheme for 6G RIS-assisted channel measurements is proposed, which is realized by the multipath components (MPCs) tracking and detection, the maximum likelihood (ML) estimation, and other procedures. It is capable of detecting whether MPCs interact with RIS and estimating all important channel parameters, including the angle parameters at the RIS side. Finally, the proposed scheme is evaluated through synthetic channels. The impacts of the RIS size, the number of transmission modes, and the phase quantization on its performance are investigated. Simulation results show a good performance of the proposed scheme.

Considering great benefits brought by massive multiple-input–multiple-output (MIMO) technologies in the Internet of Things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical versus analytical method and deterministic versus stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical nonpredictive channel models, which further clarify and combine the deterministic versus stochastic and physical versus analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical nonpredictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include the correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam-domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given.

Reconfigurable intelligent surfaces (RISs) are 2-D metasurfaces, which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth-generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this article, we aim at addressing channel characterization and modeling issues of RIS-assisted wireless communication systems. First, the concept, principle, and potential applications of RIS are given. An overview of RIS-based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS-based channel characteristics are studied, including reflection and transmission, the Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field, near field, and so on. RIS-based channel modeling works are investigated, including large-scale path loss models and small-scale multipath fading models. Finally, future research directions related to RIS-assisted channels are also discussed.

In this paper, a three-dimensional (3D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed. The angular distributions of clusters in both the horizontal and vertical planes are jointly considered. The receiver and clusters can be moving, which makes the model more general. Parameters, including number of clusters, powers, delays, azimuth angles of departure (AAoDs), azimuth angles of arrival (AAoAs), elevation angles of departure (EAoDs), and elevation angles of arrival (EAoAs) are time-variant. The cluster time evolution is modeled using a birth-death process. Statistical properties, including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), Doppler power spectrum density (PSD), level-crossing rate (LCR), average fading duration (AFD), and stationary interval are investigated and analyzed. The LCR, AFD, and stationary interval of the proposed channel model are validated against the measurement data. Numerical and simulation results show that the proposed channel model has the ability to reproduce the main properties of real non-stationary channels. Furthermore, the proposed channel model can be adapted to various communication scenarios by adjusting different parameter values.

Massive multiple-input multiple-output (MIMO) technology and millimeter wave (mmWave) communication are key technologies for the fifth generation (5G) wireless communications. The combination of mmWave and massive MIMO has the potential to dramatically improve wireless access and throughput performance. Such systems benefit from large available signal bandwidths and small antenna form factor. In the literature, most of the massive MIMO channel measurements are carried out at sub-6 GHz frequency bands, while the effects caused by large antenna arrays at mmWave bands have not been studied yet. In this paper, we conduct channel measurements at 11, 16, 28, and 38 GHz frequency bands combined with large antenna arrays in an indoor office environment. The space-alternating generalized expectation-maximization (SAGE) algorithm is applied to obtain the multipath component (MPC) parameters. New propagation characteristics like spherical wavefront, cluster birth-death, and non-stationarity over antenna array axis are validated for the four mmWave bands by investigating the temporal-spatial channel characteristics like power delay profile (PDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS). The results indicate that massive MIMO effects should be fully considered for mmWave channel models under systems with large antenna arrays.

Millimeter wave (mmWave) communication has been a key technology for the fifth generation (5G) wireless communications. There have been various mmWave channel measurements. However, many measurements in the literature are conducted with different configurations, which may have large impacts on the propagation channel characteristics, and make the comparison of propagation channel characteristics for different mmWave bands impossible. In this paper, we carry out channel measurements at 11, 16, 28, and 38 GHz bands in an indoor environment using a vector network analyzer (VNA). The space-alternating generalized expectation-maximization (SAGE) algorithm is used to obtain the multipath component (MPC) parameters including three dimensional (3D) angular domain information. The propagation characteristics like average power delay profile (APDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS) are shown and compared for the four frequency bands. The results show similar properties for different bands and indicate the possibility of the derivation of a unified channel model framework for 10-40 GHz bands.

Most millimeter wave (mmWave) channel measurements are conducted with different configurations, which may have large impacts on propagation channel characteristics. In addition, the comparison of different mmWave bands is scarce. Moreover, mmWave massive multiple-input multiple-output (MIMO) channel measurements are absent, and new propagation properties caused by large antenna arrays have rarely been studied yet. In this paper, we carry out mmWave massive MIMO channel measurements at 11-, 16-, 28-, and 38-GHz bands in indoor environments. The space-alternating generalized expectation-maximization algorithm is applied to process the measurement data. Important statistical properties, such as average power delay profile, power azimuth profile, power elevation profile, root mean square delay spread, azimuth angular spread, elevation angular spread, and their cumulative distribution functions and correlation properties, are obtained and compared for different bands. New massive MIMO propagation properties, such as spherical wavefront, cluster birth-death, and non-stationarity over the antenna array, are validated for the four mmWave bands by investigating the variations of channel parameters. Two channel models are used to verify the measurements. The results indicate that massive MIMO effects should be fully characterized for mmWave massive MIMO systems.