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.

To achieve the ambitious goals of global-coverage, complete applications, and all-spectra in the sixth generation (6G) mobile networks, channel modeling and characteristics analysis are crucial. This paper proposes an unmanned aerial vehicle (UAV)-to-ground channel model with massive multipleinput multiple-output (MIMO) configuration. Different types of three-dimensional (3-D) UAV trajectories are modeled based on an aeronautic smooth turn random mobility model (ST-RMM). Various types of antenna configurations are also considered in the model. This model is a 3-D geometry-based stochastic model (GBSM) founded on the 6G pervasive channel model (6GPC) to satisfy the requirements of 6G wireless communication systems. The generation method of large-scale parameters (LSPs) and small-scale parameters (SSPs) of the model is introduced, and the space-time cluster evolution process is modeled. Statistical properties including root-mean-square (RMS) delay spread, spacetime correlation function (STCF), stationary interval (SI), and channel capacity are also simulated and analyzed.

The sixth generation (6 G) communications will achieve broader coverage and support more communication scenarios. Satellite communications, which are important complements to ground communications, are expected to realize global coverage. This paper aims to propose a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for satellite-to-maritime channel. The proposed model consists of line-of-sight (LoS) component, single-bounce (SB) components caused by the sea surface and ship body, and multi-bounce (MB) components due to ducting effect. The satellite trajectory and arbitrary movement of ship are also taken into consideration. The model considers atmosphere effect and rain effect, which are not negligible in satellite communication systems. Finally, statistical properties such as space-time-frequency (STF) correlation function, root mean square (RMS) delay spread, and Doppler power spectral density (PSD) are derived and analyzed.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for reconfigurable intelligent surface (RIS) assisted massive multiple-input multiple-out (MIMO) communication systems is proposed. The model includes a more general path loss model and a small-scale fading channel model that considers the RIS electromagnetic (EM) response and RIS polarization adjustment. In the path loss model, a method for generating large-scale parameters with height-dependent is proposed. In the small-scale fading model, four different propagation conditions are given to reflect the diverse link states among base station, RIS, and user equipment, depending on whether the sub-channel is line-of-sight (LOS) or non-line-of-sight (NLOS). Different from the conventional phase shift (PS) model, the RIS response matrix in the small-scale fading channel model can distinguish EM waves scattered from different directions. The accuracy of the proposed channel model is further verified by real channel measurements. The results can well guide the practical deployment of RIS in future sixth generation (6G) wireless communication systems.

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.

To provide customized high-quality services for all users in the sixth-generation (6G) wireless communication systems, it is fundamental to study all 6G channel scenarios and establish accurate channel models for these scenarios correspondingly. However, the absence of comprehensive 6G scenario categorization and the difficulties of modeling the channels for all scenarios bring huge challenges. In this article, we aim to give a thorough overview of channel scenarios, identification algorithms, and intelligent channel modeling theories. First, different standardized scenario categorization principles are reviewed. A unified and exclusive scenario categorization method is elaborated with detailed 6G scenario definitions. Second, scenario features, feature selection principles, ML-based identification algorithms, as well as data preprocessing methods are surveyed for the benefit of accurate scenario identification. Third, the intelligent scenario adaptive channel modeling theory based on 6GPCM is specified. Statistical properties for industrial IoT and HST scenarios are simulated and compared with those from measurements. Finally, future research directions and challenges are addressed.

The sixth generation (6G) pervasive channel model (6GPCM) can characterize channels for all spectra from the sub-6 GHz band to the visible light communication (VLC) band and all scenarios, such as maritime, (ultra-)massive multiple-input multiple-output (MIMO), and industrial Internet of things (IIoT) communication scenarios in 6G wireless systems. The unified channel model can enable us to analyze channel statistical properties in systems using different scales of antenna arrays, different frequency bands, and different scenarios with different movement speeds. In this paper, we conduct a complete study on space-time-frequency (STF) statistical properties of the 6GPCM. Mathematical derivations and simulations are provided, including STF correlation function (STFCF), spatial/temporal/frequency correlation functions, angular/Doppler/delay power spectral densities (PSDs), root mean square (RMS) angular/Doppler/delay spreads, coherence distance/time/bandwidth, stationary distance/time/bandwidth, and level-crossing rates (LCRs)/average fade durations (AFDs) in STF domains. In addition, we classify these statistical properties according to their definitions and then reveal the complex relationships between them and channel model parameters. This work will lay a solid foundation and offer useful guidelines for research on 6G wireless communication systems.