To advance research on maritime communications in sixth generation (6G) communication networks, a general three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed in this paper. The proposed model is applicable to a wide range of communication scenarios in maritime internet of things (IoT) communication networks, which involve diverse communication terminals including unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs)/ships, and shore-side base stations. It employs a comprehensive channel modeling framework to simulate space-time-frequency non-stationary properties in various maritime communication scenarios. Based on the sea spectrum, factors such as sea surface scattering, onboard array rotation, and line-of-sight (LoS) communication probability are jointly considered. Moreover, the proposed channel model incorporates distinct features of maritime channels, including UAV rotation, coastal building interference, and evaporation duct signal propagation, as observed in diverse scenarios. Utilizing the proposed channel model, we derive and simulate the channel statistical properties, such as space-time-frequency correlation function (STF-CF), angular power spectrum density (PSD), and root-mean-square (RMS) delay spread, as well as the channel capacity. We conduct an in-depth investigation to analyze effects of different channel parameters on statistical characteristics and channel capacity. Also, the good agreement between the measurement results and corresponding simulation results confirms the model’s applicability.

Integrating multi-modal environment sensing and wireless channel prediction provides an innovative unmanned aerial vehicle (UAV) channel modeling solution, which can serve as a foundation for future UAV communication system design and network optimization. This paper proposes a novel UAV channel predictive model using multi-modal environment sensing information and measured channel data. To enable comprehensive understanding of the complex and dynamic UAV communication surroundings, the Global position system (GPS) location data, inertial measurement unit (IMU) data, channel data, and environment information are fused as the model’s input. Within a generative adversarial network (GAN) framework, the model enhances the prediction accuracy of channel data in complex environments through adversarial training of the generator and discriminator, producing channel data as the model’s output that closely approximates real-world measurements. Furthermore, visual perception data from UAVs is incorporated into the prediction process, allowing the model to abstract scatterers by capturing changes in environment obstacles. During channel prediction, an attention mechanism is also incorporated into the multi-modal data fusion process, dynamically adjusting the importance of each modality to ensure the model concentrate on the most crucial features. Results from the experiments indicate that the proposed method facilitates real-time predictions of ground channel data across a range of flight altitudes and communication frequencies. This advancement contributes important knowledge to UAV communication studies and significantly boosts the effectiveness and reliability of intelligent air-to-ground communication networks.

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.

A channel prediction modeling method based on multimodal fusion perception is proposed for complex environments in unmanned aerial vehicle (UAV) air-to-ground (A2G) communications. Two-dimensional (2D) environmental information and three-dimensional (3D) point cloud data are fused to enhance the model’s ability to capture environment blockage, reflection, and multipath effects. The 2D information provides target objects’ planar distribution and texture features, while the 3D point clouds supplement spatial position, size, and height information. These complementary modalities comprehensively describe the geometric structures present in complex environments. A multimodal modeling network is constructed to explore the nonlinear mapping between environmental perception data and channel data. The network takes 2D building distribution, 3D point cloud data, global image features, UAV and receiver positions, and communication parameters as joint inputs. Feature extraction and fusion modules achieve effective joint encoding of heterogeneous multimodal features. A spatial feature decoupling (SFD) module is designed to address interference caused by coupled features. It separates the data distributions corresponding to different channel characteristics, improving the accuracy of channel impulse response (CIR) prediction. Experimental results demonstrate that the proposed method significantly improves the reliability and adaptability of UAV channel modeling in complex urban scenarios.

For future sixth generation (6G) space-air-ground-sea integrated networks, leveraging troposcatter for beyond line-of-sight (BLoS) propagation offers a promising solution to achieving long-range unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) multiple-input multiple-output (MIMO) geometry based stochastic model (GBSM) is proposed for long-range UAV-to-ground (U2G) communications. The proposed channel model incorporates UAV 3D mobility and troposcatter characteristics, enabling a distance-dependent transition from visual-range to BLoS communications. A path loss model that accounts for stochastic obstacle deployments and troposcatter characteristics is developed, and the corresponding link budget is calculated to evaluate the feasibility of BLoS communications. Furthermore, troposcatter clusters are modeled and their space-time evolution is characterized to capture channel non-stationarity. Statistical properties, including space-time-frequency correlation function (STF-CF), delay/Doppler power spectral densities (PSDs), root mean square (RMS) delay/Doppler spreads, and coherence bandwidth/time, are also derived and analyzed. The proposed channel model provides valuable guidance for the design of long-range U2G communications.

With over 80% of mobile traffic occurring indoors, optimizing indoor wireless communication performance is crucial. For indoor communication systems, interior walls play a significant role in shaping system performance, yet their impacts on wireless communication performance at the link level remain insufficiently explored. In this paper, we propose an evaluation method for indoor wireless performance, considering wall materials and locations. Based on the evaluation method, the relationships between interior walls and signal propagation are investigated, considering line-of-sight (LOS) probability, blockage height, blockage density, and wall materials. The signal propagations are divided into four cases, including the direct path, the reflection path and/or penetration path through walls, and the multipath from scatterers. Moreover, spatial cross-correlation function (SCCF), spectrum efficiency (SE), and energy efficiency (EE) are analyzed to unveil the impacts of walls on channel characteristics and system performance. Our findings reveal that strategically adjusting the placement and materials of walls, while keeping network settings unchanged, can significantly enhance system performance.

The integration of sub-6 GHz, centimeter-wave, and millimeter-wave bands through cross-band coordination is recognized as a pivotal approach to optimize system performance in the sixth generation (6G) research. The existing standard channel models, e.g., 3GPP TR 38.901 and QuaDRiGa, support cross-band channel simulations. However, both models assume that multipath components observed at different bands are identical, which is inconsistent with channel measurements. In this paper, we propose a general cross-band channel model, which considers the birth and death of clusters across different bands. The evolution of clusters in temporal and spatial domains is also incorporated, where the common clusters observed at different bands are modeled to undergo identical birth-death processes. Furthermore, the frequency-dependent blockage losses caused by obstacles are embedded in the proposed model. A comprehensive analysis of channel statistical properties is conducted, including the delay/angular/Doppler spreads and stationary times/distances. Moreover, the similarities of cross-band channels in angular and delay domains are analyzed. To validate the accuracy of the proposed model, cross-band channel measurements across 0.7 to 39 GHz are conducted by time-domain channel sounders, which are capable of simultaneously measuring channels at two bands. Comparisons of simulation results from the proposed model, enhanced 6G pervasive channel model, and 3GPP TR 38.901 with measurement data reveal that the proposed model demonstrates the best alignment with measurements.

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.

Massive multiple-input multiple-output (MIMO) is considered as a key technology in the sixth generation (6 G) communication systems, bringing unique channel characteristics such as spherical wavefront and spatial non-stationarity effects. In this paper, we propose a super-resolution ray launching (SRRL) method to efficiently and accurately model the channel characteristics of massive MIMO scenarios. SR-RL simplifies simulations by performing RL only on the selected antenna elements based on the coherence distance and ignoring other elements. Ray paths traced from the selected elements are distributed to the ignored elements using the reflecting planes and possible obstacles along the ray trajectory. The obtained ray paths of both selected and ignored elements are recorded in a table, revealing a pattern of changes in multipath components across the antenna array. Compared to traditional brute-force methods, SR-RL can reduce computational time significantly, and captures spherical wavefront and spatial non-stationarity effects without sacrificing accuracy.

Reconfigurable intelligent surface (RIS) technology has emerged as a key direction for 6 G mobile communication systems due to its revolutionary potential in channel control, coverage enhancement, and energy efficiency optimization. In this paper, a novel ray tracing (RT) channel model for RIS-based beamforming at the transmitter (Tx) side is proposed. The model considers line-of-sight (LoS) conditions for both the Tx and receiver (Rx). At the Tx side, the feed antenna directs the beam toward the RIS, ensuring that at least one reflected ray from the center of the RIS reaches the Rx. The Rx captures the reflected rays arriving from the direction of the RIS. The statistical properties of the channel model for RIS-based beamforming in indoor scenarios are analyzed including path loss, delay power spectral density (PSD), and angular PSD. The statistical properties of the proposed channel model can well matched the channel measurements.

The explosive growth of the sixth-generation ( 6 G ) wireless system necessitates efficient base station (BS) deployment to balance coverage, data rates, and economic costs. In this paper, we propose a novel BS deployment scheme that jointly optimizes the number and locations of the deployed BSs, considering limited BS throughputs and practical non-uniform user distributions. A variant multi-dimensional knapsack problem is formulated and then solved by proposing a novel depth-limited backtracking dynamic programming (DLB-DP) algorithm with a BS-user association (BSUA) algorithm. We compare the proposed DLB-DP algorithm with three state-of-the-art benchmark algorithms in a hotspot scenario. The results demonstrate that the proposed algorithm outperforms the considered alternatives in terms of coverage, data rates, and robustness under varying user densities.

The separation of antenna design and channel modeling has made it difficult to capture the intricate interplay between antenna parameters and channel dynamics, resulting in suboptimal performance in real-world environments. To address this challenge, this study introduces a stochastic framework that integrates random antenna array, modeled as one-dimensional Wiener process, with a realistic wireless propagation channel model, i.e., the sixth-generation pervasive channel model (6 GPCM). This framework is grounded in rigorous derivations and incorporates reasonable approximations. It provides a detailed analysis of how antenna mobility, characterized by the increasing variance of the Wiener process over the same time interval, influences the spatial cross-correlation function (SCCF), temporal autocorrelation function (TACF), and channel capacity. Finally, based on the aforementioned derivations, simulations are conducted to validate the theoretical findings, and the results are analyzed to demonstrate their implications for wireless communication system design.

Partition-based clustering algorithms like K-Means, KPowerMeans (KPM) and fuzzy c-means (FCM) have drawbacks for identifying arbitrarily sized non-spherical clusters in supporting wireless channel modeling. In this paper, we improve the Mahalanobis distance metric and propose a novel space-transformed and weighted fuzzy c-means (STW-FCM) algorithm for clustering multipath components (MPCs). The proposed STW-FCM algorithm imposes more relaxed assumptions on cluster shapes, allowing better accommodation of irregularly shaped clusters. Both synthetic and channel measurement data are utilized for validating the proposed STW-FCM algorithm, demonstrating its superior performance compared to existing clustering algorithms.

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.

The Industrial Internet of Things (IIoT) is a promising scenario for Industry 4.0, where the existence of rich scatterers can affect the quality of communication in the factory. In this paper, we investigate the multiple base stations (BSs) deployment problem based on the ray tracing (RT) method in IIoT scenarios considering coverage. We simplify the construction of IIoT scenarios by considering the geometric and channel characteristics to improve computational efficiency in RT simulation. Then, the BS deployment problem is formulated subject to the path loss and energy efficiency. The particle swarm optimization (PSO) algorithm with the surrogate model from RT simulation is proposed to solve the formulated problem. Moreover, the surrogate model is used to replace the all-time RT simulation in the PSO algorithm. The simulation results demonstrate that the proposed method effectively addresses BS deployment challenges in IIoT scenarios.

Underwater acoustic (UWA) channel models are indispensable for the design of UWA communication systems and technologies. In this paper, a novel non-stationary three-dimensional (3D) twin cluster geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) UWA communication systems in shallow seas. The distribution of non-line-of-sight (NLoS) delays obeys the Nakagami distribution in this model according to the results generated by Bellhop. In addition, the relationship between delay and power is modeled as a negative exponential distribution with different parameters for each NLoS component. The periodic mobility of clusters and distribution of scatterers, caused by the fluctuations of sea surface, are considered to account for the unique UWA environment. Channel statistical properties, such as the space-time-frequency correlation function (STFCF), Doppler power spectrum density (PSD), Doppler spread, coherence time, and coherence distance, are investigated. In particular, temporal autocorrelation function (TACF) and frequency correlation function (FCF) are compared with measurement data to validate the accuracy of this model. Simulation results show that the fluctuating sea surface can cause significant changes in UWA channel characteristics, making it indispensable in channel modeling.

Unmanned-aerial-vehicle (UAV) communications, as a critical application scenario in the sixth generation (6G) wireless communication field, has garnered widespread attention. During UAV-to-ground communication, channel data plays a pivotal role. Analyzing channel data enables an understanding of communication environments’ diversity and temporal variability, thereby facilitating the construction of more efficient communication systems. This article proposes a novel UAV-to-ground channel prediction method based on multimodal fusion. The method aims to achieve real-time and precise prediction of UAV-to-ground channel data from UAVs in the 3-D airspace by integrating various sources of information, including UAV-captured images, location data of transmitters and receivers, and communication settings. The network uses a fused architecture combining convolutional neural network (CNN) and Transformer architecture to extract and integrate features from diverse information sources. This fusion strategy significantly enhances the accuracy of UAV-to-ground channel prediction. Incorporating image information enables the network better to comprehend the complexity and dynamics of communication environments, thereby assisting in achieving more precise UAV-to-ground channel prediction. Experimental results demonstrate that the proposed method achieves real-time prediction of ground channels across various flight altitudes and communication frequency bands. This provides robust technical support for advancing UAV communication and offers new insights for optimizing and upgrading future wireless communication systems.

For the development of the sixth-generation (6G) wireless communication technologies and the realization of integrated space–air–ground–sea networks, a novel nonstationary unmanned aerial vehicle (UAV) to multi-unmanned surface vehicle (multiUSV) channel model for maritime communications is proposed. The impacts of evaporation duct and sea surface scattering paths on the channel are considered in the model. To describe the related cooperative, noncooperative, and original characteristics among different UAV-to-unmanned surface vehicle (USV) channels, a cooperative degree parameter based on power distribution is introduced as an influencing factor. For classifying cooperative clusters, the KPowerMeans (KPM) algorithm and birth–death (B–D) process are applied to extract and cluster the cooperative and noncooperative paths, followed by their evolution. Furthermore, the channel impulse responses (CIRs) of the cooperative, noncooperative, and original channels are represented. The distribution of cooperative clusters is then analyzed and fitted by the normal distribution. Several typical statistical properties of the proposed channel model, including auto-correlation function (ACF), cross-correlation function (CCF), root mean square delay spread (RMS DS), stationary intervalss (SIs), cooperative degree, and channel correlation across different USVs, are examined. The effects of speed and altitude on specific channel characteristics are also analyzed. Finally, the model’s accuracy is verified through a comparison of available measured and simulated data. This model provides theoretical guidance for the design and evaluation of future 6G maritime multiuser cooperative communication systems.

With the development of sixth-generation (6G) wireless communication technology, establishing an accurate and effective channel model has become an indispensable technical foundation for the exploitation of novel systems. However, traditional wireless channel modeling methods display obvious deficiencies in the face of more complex scenarios and massive data requirements in 6G communication. In view of this, a novel generative adversarial network (GAN)-based channel generation and modeling method for 6G intelligent industrial internet of things (IIoT) communications is proposed, which is validated with the measurement data obtaining from multi-frequency and multi-scenes IIoT channels. In this methodological framework, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is employed to make predictions on real measurement data. It enables the reconstruction of lost channel data and data augmentation, which effectively address the problem of shortage of the real measurement data. Using the generated channel data, the birth-death (B-D) process of clusters is tracked and then a cluster-based channel model is constructed for 6G intelligent IIoT communicaitons. The characteristics and the survival distance of clusters with different scenes in different frequency bands are further investigated and analyzed. The relevant research provides an innovative approach for channel generation and modeling of 6G IIoT scenarios.

With the development of the sixth-generation (6G) wireless communications and Industrial Internet of Things (IIoT), numerous sensors, smart devices, and mobile terminals will be deployed in factories. Accurate modeling of IIoT channels facilitates the design and evaluation of the communication system, thereby ensuring the stability and efficiency of IIoT production systems. However, the huge data volume, the high dynamism, and complexity of IIoT communication environments pose significant challenges for traditional channel models in accurately capturing their characteristic variations. To address these issues, a deep-learning (DL)-based multimodal fusion predictive channel model is proposed in this article. The model is designed with the convolutional neural network (CNN) and multilayer perceptron (MLP) to extract feature information from input dual-camera images and basic physical parameters, respectively, and utilizes the fused features to predict received power and root-mean-square delay spread (RMS DS) in factory environments. The model integrates multiple modal information, capable of fully capturing the complex mapping relationship between the environment and channel characteristics. Comprehensive experiments demonstrate that the model achieves optimal prediction performance when integrating image information from both the transmitter (Tx) and receiver (Rx) simultaneously. Compared to several existing methods, our proposed predictive model exhibits superior performance. It presents a practical and feasible solution for the design and optimization of advanced IIoT systems in the future.

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.

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.

In this paper, we study the impacts of user mobility on the average bit error probability (ABEP) and average achievable data rate (ADR) of ultra-reliable and low-latency communication (uRLLC) systems. Specifically, based on the time autocorrelation function (ACF) of the sixth generation pervasive channel model (6GPCM), we derive the expression of the pairwise error probability (PEP) of uRLLC systems adopting the M-ary phase shift keying (MPSK) modulation. In addition, we propose a new framework for the calculation of the average ADR considering the channel estimation period and cyclic prefix (CP) length. Moreover, the calculation methods of maximal data transmission period and transmission latency are given. Numerical results show that the increasing user mobility degrades the ABEP and average ADR. Impacts of user mobility on the ABEP surpass its influences on the average ADR. Furthermore, the tightness of analytical results is validated by Monte Carlo simulations. The performance analysis will guide the flexible designs of transmission blocklength under different reliability constraints.

The sixth-generation (6G) systems will adopt space- air-ground-sea integrated communications to achieve ubiqui- tous connectivity. In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for satellite-unmanned aerial vehicle (UAV)- ground communications. This model considers low earth orbit (LEO) satellites and UAVs as base and mobile relay stations. Millimeter wave (mmWave) bands, terminal positions, arbitrary movement, and elevation angle are described in the model based on standardized large- scale parameters (LSPs). The channel is further analyzed in space, time, and frequency for the proposed model to describe the channel characteristics for delay spread (DS), angular spread (AS), and Doppler spread. The results show that long propagation distance and rapid mobility exhibit significant Doppler shift effects, while adding UAV relay mobile station (Mx) could mitigate. Also, elevation and arrival angle of Mx are impacted by its aerial position.

Base station (BS) deployment is not a one-time endeavor, as when transitioning to higher frequency bands, coverage holes may arise, and the initial deployment may be unsatisfactory. In such cases, the deployment of additional BSs may be necessary. In this paper, we formulate an optimization problem to deploy additional BSs, attempting to minimize overlap coverage rate while satisfying the limitations of coverage rate and spectral efficiency. An improved genetic algorithm is adopted to solve the optimization problem. Numerical results show that the improved genetic algorithm can effectively minimize the overlap coverage rate and outperforms the original genetic algorithm and the particle swarm optimization (PSO) algorithm in terms of the minimum overlap coverage, spectral efficiency, the number additional BSs, and the convergence speed.

In this paper, a geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) maritime multi-user land-to-ship communication channels. The inter-link correlation between different users is modeled by spatial correlated large scale parameters (LSPs). In addition, the evaporation duct is modeled based on a twin-cluster model, and the signal propagation between two clusters is simulated by several-order reflections to obtain the delay and angle parameters of each ray. Simulations based on the proposed model focus on analyzing the statistical properties that reflect the spatial correlation between multiple users. Effects of the number of antennas, multipath components (MPCs), and different heights of evaporation duct on the correlation between users and the channel capacity are investigated and corresponding conclusions are drawn.

Ray tracing (RT) is an effective deterministic channel modeling method, but the computational burden for dynamic scenarios is high. To address this issue, this study categorizes objects involved in various types of motion within dynamic scenarios based on their motion characteristics, distinguishing between static and dynamic objects. Subsequently, the subinterval of the optimal split plane suitable for bounding volume hierarchies (BVH) construction is calculated, and the simplified interval BVH algorithm (SIBVH) is applied to the vehicle-to-vehicle (V2V) communication scenario. The simulation results indicate that, SIBVH based RT algorithm can trace rays much faster than traditional RT method, and improve computational efficiency significantly. The proposed algorithm can be effectively used in complex high-mobility communication scenarios.

In massive multiple-input multiple-output (MIMO) systems, the channel estimation scheme is subject to the spatial non-stationarity and inevitably power leakage in the beam domain. In this paper, a beam domain channel estimation scheme is investigated for spatial non-stationary (SNS) massive MIMO systems considering power leakage. Specifically, a realistic mas-sive MIMO beam domain channel model (BDCM) is introduced to capture the spatial non-stationarity considering power leakage by introducing the illustration of visibility region (VR). Then, a beam domain structure-based sparsity adaptive matching pursuit (BDS-SAMP) scheme is proposed based on the cross-block sparse structure and power ratio threshold of beam domain channel. Finally, the simulation results validate the accuracy of proposed BDS-SAMP scheme with low pilot overhead and reasonable complexity by comparing with conventional schemes.

As an indispensable part of future sixth generation (6G) wireless communication, unmanned aerial vehicle (UAV) communication has become an inevitable trend and developed rapidly. The channel emulator is an effective and resource-saving tool to evaluate the designed UAV communication systems. In this paper, a novel UAV multi-trajectory dual-mobility channel model is proposed. Then, based on the Coordinate Rotation Digital Computer (CORDIC) method, the hardware implementation of the proposed model is developed in the Field Programmable Gate Array (FPGA). As the hardware output data of channel emulator, the channel impulse responses (CIRs) are acquired. Furthermore, the corresponding power delay profile (PDP) and root-mean-square (RMS) delay spread calculated by the CIRs are analyzed. Finally, by comparing with the simulation results of proposed channel model, the availability and accuracy of the channel emulator is validated.

In the sixth generation (6G) wireless communication systems, the communication coverage range becomes broader, where transmitters (Tx) and receivers (Rx) can be randomly distributed in continuous space. The continuous-space electromagnetic (EM) channel is different from the conventional propagation channel. Its input is continuous current density and its output is continuous electric field so that channel characteristics at any point can be given under the guidance of EM information theory (EIT). EIT is a theory integrating traditional theories applied in communications, such as EM theory and information theory. In this paper, a novel 6G continuous-space EM channel model jointly considering antenna response (AR), mutual coupling (MC), and small-scale fading is proposed. AR at the Tx and Rx sides is modeled using an EM calculation method. MC is obtained using antenna theory. Small-scale fading is described using a geometry-based stochastic model (GBSM). Important statistical properties and channel capacity are analyzed to reveal characteristics of the proposed channel model. The results illustrate that the antenna array has non-negligible impacts on statistical properties of the channel model and channel capacity.

The performance evaluation of sixth generation (6G) communication systems is anticipated to be a controlled and repeatable process in the lab, which brings up the demand for wireless channel emulators. However, channel emulation for 6G space-time-frequency (STF) non-stationary channels is missing currently. In this paper, a non-stationary multiple-input multiple-output (MIMO) geometry-based stochastic model (GBSM) that accurately characterizes the channel STF properties is introduced firstly. Then, a subspace-based method is proposed for reconstructing the channel fading obtained from the GBSM and a channel emulator architecture with frequency domain processing is presented for 6G MIMO systems. Moreover, the spatial time-varying channel transfer functions (CTFs) of the channel simulation and the channel emulation are compared and analyzed. The Doppler power spectral density (PSD) and delay PSD are further derived and compared between the channel model simulation and subspace-based emulation. The results demonstrate that the proposed channel emulator is capable of reproducing the non-stationary channel characteristics.

In the sixth generation (6G) wireless communication networks, ultra-massive multiple-input multiple-output (MIMO) communication is one of the most promising technologies. In ultra-massive MIMO channels, the mutual coupling (MC) effect is more obvious when antenna elements are more closely spaced. In this paper, a novel 6G space-time-frequency (STF) non-stationary massive MIMO channel model is proposed, which jointly considers MC, antenna efficiency, and near-field steering vectors of different antenna topologies. As the Shannon capacity theorem is based on the wide-sense stationary (WSS) channel assumption and cannot be applied to non-stationary channels, we propose a novel non-stationary channel capacity calculation method that divides the non-stationary channel into WSS sub-channels. Important statistical properties and channel capacities of the proposed channel model are derived and verified by ultra-massive MIMO channel measurements and data post-processing. The results show that the simulated spatial cross-correlation function (CCF) and channel capacity considering MC and antenna efficiency are closer to measured results. It also shows that antenna topologies have an impact on channel capacities. Furthermore, channel capacities using the proposed novel calculation method match the measured channel capacities in non-stationary channels.

It is envisioned that the sixth generation (6G) and beyond 6G (B6G) wireless communication networks will enable global coverage in space, air, ground, and sea. In such networks, both base stations and users can be mobile and will tend to move continuously in three-dimensional (3D) space. Therefore, obtaining channel state information (CSI) in 3D continuous-space is crucial for the design and performance evaluation of future 6G and B6G wireless systems. On the other hand, new 6G technologies such as integrated sensing and communications (ISAC) will also require prior knowledge of CSI in 3D continuous-space. In this paper, a 3D continuous-space electromagnetic channel model is proposed for tri-polarized multi-user communications, taking into account scatterers and spherical wavefronts. Scattered fields are calculated using the method of moments (MoM) with high accuracy. Spherical wave functions are utilized to decompose the dyadic Green’s functions that connect the transmitted source currents and the received electric fields. Simulation results demonstrate that transmit power, apertures, scatterers, and sample intervals have significant impacts on statistical properties and channel capacities, providing insights into the performance of continuous-space electromagnetic channel models and the design of future wireless systems.

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.

Spatial domain is explored to improve system performance in the sixth generation (6G) wireless communication system. Massive multiple-input multiple-output (MIMO) and orbital angular momentum (OAM) are key technologies to achieve this purpose. In this paper, a novel beam domain channel model (BDCM) for OAM communication systems with massive uniform circular array (UCA) is proposed. The generation of OAM waves proves feasible for UCA. Frequency invariant beamforming (FIBF) is applied to transform OAM basis to plane-wave or spherical-wave basis to make full use of the spatial domain in the multipath scenario. Furthermore, near-field characteristic and mutual coupling (MC) are considered in this model. Statistical properties like temporal autocorrelation function (TACF) and beam spread are analyzed and simulated. Complexity of the proposed BDCM is evaluated. Non-negligible impacts caused by the antenna like configuration and MC on TACF are revealed.

As one of the most promising technologies to fulfill the vision for the sixth-generation (6G) networks, holographic multiple-input multiple-output (MIMO) is a significant technology under research. In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for holographic MIMO communications that includes mutual coupling (MC) is proposed. The holographic MIMO is modeled as an antenna array in compact area with limited element spacing. With comprehensive considerations of the factors of scatterers distribution, geometry relationships, and coupling coefficients, the channel impulse response (CIR) and statistical properties of the proposed channel model including the space-time correlation function (STCF), level crossing rate (LCR), average fade duration (AFD), and instantaneous channel capacity are investigated. Compared to the modified method of the equal area (MMEA) based simulation model, the simulation results demonstrate that the statistical properties of our proposed model confirms the theoretical results well and holographic MIMO has the potential to increase channel capacity.

With the improvement of hardware performance, graphics processing unit (GPU) has been applied to the acceleration of ray tracing (RT) wireless channel modeling. In this paper, a new RT acceleration algorithm based on message passing interface (MPI) and GPU called MPI fused with GPU tree algorithm (MGTree) is proposed. Moreover, the indoor conference room is modeled, and the forward algorithm shooting and bouncing ray (SBR) is used for RT. Considering the conventional reflection and diffraction, the three-dimensional (3D) RT acceleration algorithm based on the unified computing device architecture (CUDA) is used to optimize the conventional algorithm, which combines GPU with MPI to form multi-core distributed computing. Then field information is extracted at the receiving side and the wireless channel simulations are conducted. Through experiments, the new algorithm can effectively improve the upper limit of the acceleration ratio, support more rays, and alleviate the pressure of a single GPU kernel.

Vehicle-to-vehicle (V2V) is an important sixth generation (6G) communication scenario, and we need to measure the performance of V2V communication systems in a controlled and repeatable channel environment. Hence, a corresponding channel emulator is required. In this paper, we first introduce a non-stationary V2V geometry-based stochastic model (GBSM) that takes into account the birth-death process of clusters and adapts to changes of the V2V scenario. Furthermore, the adaptive non-stationary V2V channel emulator with the birth-death process of clusters is constructed on a field-programmable gate array (FPGA) hardware platform. This channel emulator uses the fading channel generation algorithm of sum-of-frequency-modulation (SoFM) and the sine wave generation algorithm of the look-up table (LUT). Moreover, the important statistical properties of the channel under the theoretical simulation and hardware platform emulation are studied through simulations, e.g., temporal autocorrelation function (TACF), spatial cross-correlation function (SCCF), and delay power spectral density (delay PSD). Results show that the proposed channel emulator and the corresponding theoretical channel model fit well.

Nowadays, Reconfigurable intelligent surface (RIS) is regarded as one of the key technology of the sixth generation (6G) wireless communication systems. However, most of the current researches are based on single RIS. In this paper, we propose a three-dimensional (3D) double-RIS-assisted geometry-based stochastic model (GBSM) for massive multiple-input multiple-output (MIMO) communication systems. The channel model also supports the movements of transmitter, receiver, and clusters. For RIS, a new method is proposed for the joint design of reflection coefficients in MIMO channels based on cascaded RISs. In addition, different channel properties in the spatial domain, time domain, and frequency domain are studied to verify the validity and non-stationary properties of the model and explore the improvement of channel performance by double RISs.

Integrated sensing and communication (ISAC) has attracted wide attention as an emerging application scenario for the sixth generation (6G) wireless communication system. In this paper, a novel three-dimensional (3D) non-stationary localization-assisted ISAC geometry-based stochastic model (GBSM) is proposed. The locations of the first-bounce scatterer and last-bounce scatterer in the communication channel can be estimated by the particle filter with the assistance of backscattering sensing. The important channel statistical properties of the proposed channel model are simulated and compared with the ray tracing (RT) results, including the delay spread, azimuth angle of departure/arrival (AAoD/AAoA) spread, and elevation angle of departure/arrival (EAoD/EAoA) spread. The simulation results of the proposed channel model show a good agreement with the RT results, which proves the correctness of the proposed channel model. Utilizing the localization parameters of scatterers, the proposed ISAC channel model can better map the real environment.

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.

In this paper, we study the spectrum efficiency (SE), energy efficiency (EE), and economic efficiency (ECE) for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems. For outdoor scenarios, massive multiple-input-multiple-output (MIMO) technologies and distributed antenna systems (DASs) at sub-6 GHz frequency bands are used for long-distance communications. For indoor scenarios, millimeter-wave (mmWave) and beamforming communication technologies are deployed at wireless indoor access points (IAPs) to provide high-speed short-range services to indoor users. Mathematical expressions for the system capacity, SE, EE, and ECE are derived using a proposed realistic power consumption model. The results shed light on the fact that the proposed network architecture is able to improve SE and EE by more than three times compared to those conventional network architectures. The analysis of system performance in terms of SE, EE, ECE, and their trade-off results in the observation that the proposed network architecture offers a promising solution for future B5G communication systems.

The integrated sensing and communication (ISAC) refers to the integration of radio sensing and wireless communications to realize the multiplexing of space, time, and frequency resources. In the sixth generation (6G) wireless networks, ISAC is considered as one of the most promising technologies. In this paper, a novel ISAC channel model combining forward and backward scattering is proposed. In addition to the non-stationarity caused by motions, the correlations between sensing and communication channels are investigated. The channel characteristics of sensing such as forward scattering and backward scattering are introduced into the communication channel model. Utilizing the correlations between sensing and communication channels, the communication channel model is divided into line-of-sight (LOS), forward scattering, and backward scattering components. These three components are summed according to probability weighting to obtain a more accurate channel model for sensing assisted communication systems. Moreover, important statistical properties of the proposed ISAC channel model are derived and simulated. The analytical and simulation results match well, demonstrating the correctness of derivations and simulations. Some derived/simulated statistical properties are verified by corresponding measurement data, which indicates the utility of the proposed ISAC channel model.

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.

Reconfigurable intelligent surface (RIS) is a potential solution to cost-effectively and energy-efficiently improve the performance of the sixth generation (6G) wireless communication system. In this paper, a method to realize the function of RIS in three dimensional (3D) ray tracing simulator is proposed and applied to two indoor scenarios where there is no line-of-sight (LoS) path between transmitter (Tx) and receiver (Rx). To verify the effectiveness of the proposed method, channel characteristics including received power, channel capacity, and angular power spectral density (PSD) are studied. To analyze the influence of different RIS deployment locations on coverage enhancement, simulations with different setups are carried out at 5.4 GHz. Besides, applicability of RIS in millimeter wave (mm Wave) is also demonstrated in ray tracing simulator. Numerical results show a significant coverage improvement by adding RIS into current wireless communication system and reveal useful insights for optimal RIS deployment in indoor scenarios.

With the development and popularization of massive multiple-input multiple-output (MIMO) technology, the spatial non-stationarity and mutual coupling (MC) of massive MIMO channels are significant. Shannon channel capacity formula assumes that the signal is wide-sense stationary (WSS), which is not applicable in non-stationary channel. In this paper, we investigate the spatial non-stationary channel capacity of massive MIMO channel model with MC. The spatial statistical properties and stationary interval (SI) are simulated and the channel capacity is derived. Compared to the WSS channel capacity, the non-stationary channel capacity is greatly improved. There is also capacity increase in channel with MC due to the decorrelation of channel coefficients. The massive MIMO channel measurement can verify the accuracy of the channel model and the correctness of the spatial non-stationary channel capacity calculation.

Compared with conventional passive channel modeling, artificial intelligence (AI) based channel models show great advantages in solving real-time prediction problems in wireless communications. In this paper, a generative adversarial network (GAN) and long short-term memory (LSTM) based channel prediction framework is proposed to model indoor wireless channels. By using GAN and LSTM, the model not only enriches the channel data but also achieves the sequence prediction, which can solve the problem of the shortage of training data and prediction channels in the space domain. The prediction performance is evaluated by comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of measured data and predicted data. By comparing the statistical properties of the channel measurement data and of the synthetic data, it can be found that the proposed model can predict unknown information in the space domain.

Industrial Internet of things (IIoT) is a typical application scenario in the sixth generation (6G) mobile networks. IIoT scenarios involve dense multipath components (MPCs) and nonnegligible scattering components caused by many moving objects. In this paper, image method (IM) is applied and extended to analyze the channel properties of IIoT. Directive model is modified to adapt to IM. The moving patterns of objects are defined and their snapshots are established along the time axis. Multiple-input multiple-output (MIMO) is supported as it is widely applied in IIoT. A smart warehouse scenario equipped with moving handcars is selected to analyze the channel of IIoT scenario. Parameters such as azimuth angle, elevation angle, angular spread, power, and delay spread of received rays are calculated and compared with those generated by quasi-deterministic (Q-D) model traditionally used in IM. Maximum and minimum Doppler shifts, received power, and delay spread are calculated along the time axis to analyze the influence of mobility to channel properties. The results show that directive model generates scattering components more realistically compared with Q-D model, and that the channel properties may experience sudden changes due to the line-of-sight (LoS) component being obstructed.

Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology are significant for the sixth generation (6G) communication systems. In this paper, we employ the shooting and bouncing ray (SBR) method integrated with GPU acceleration technology to model THz and massive MIMO channel. The results of ray tracing (RT) simulation in this paper, i.e., angle of departure (AoD), angle of arrival (AoA), and power delay profile (PDP) under the frequency band supported by the commercial RT software Wireless Insite (WI) are in agreement with those produced by WI. Based on the Kirchhoff scattering effect on material surfaces and atmospheric absorption loss showing at THz frequency band, the modified propagation models of Fresnel reflection coefficients and free-space attenuation are consistent with the measured results. For massive MIMO, the channel capacity and the stochastic power distribution are analyzed. The results indicate the applicability of SBR method for building deterministic models of THz and massive MIMO channels with extensive functions and acceptable accuracy.

Ray tracing is an efficient channel modeling method. However, the traditional ray tracing method has high computation complexity. To solve this problem, an improved bounding volume hierarchies (BVH) algorithm is proposed in this paper. Based on surface area heuristic (SAH) and spatial distance, the proposed algorithm can effectively reduce the number of unnecessary intersection tests between ray and triangular facets. In addition, the algorithm fully considers the influence of ray action range, which can not only make up for the defects of spatial division based on uniform grid method and k-dimensional (KD) tree, but also solve the problem of unsatisfactory spatial division based on traditional BVH algorithm. The simulation results show that compared with the traditional BVH algorithm, the proposed algorithm can improve the computation efficiency by 20% to 35% while ensuring the computation accuracy.

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI-based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios when the channel fingerprint database is established at 0.2 m intervals.

Three channel models in the fifth generation (5G) and beyond fifth generation (B5G) communication systems, i.e., QuaDRiGa, more general 5G channel model (MG5GCM), and B5G channel model (B5GCM) are investigated in this paper. They are geometry-based stochastic models (GBSMs) and can be applied to various frequency bands, including sub-6 GHz, millimeter wave (mmWave) bands, and terahertz (THz) bands. We analyze time evolution, space-time-frequency non-stationarity, and spherical wave-front in channel modeling. Based on channel impulse responses (CIRs), temporal autocorrelation functions (ACFs), spatial cross-correlation function (CCFs), and non-stationary multiple-input multiple-output (MIMO) channel capacities are simulated. The results demonstrate that the statistical properties of the simulation model are in good agreement with the statistical properties of the theoretical model, indicating the correctness of three channel models. The proposed novel channel capacity method is closer to channel measurement data than traditional capacity in non-stationary channel. The capacity of non-stationary MIMO channel is higher than the capacity of a WSS channel.

Compared with image method (IM) based ray tracing (RT), shooting and bouncing ray (SBR) method is characterized by fast speed but low accuracy. In this paper, an iterative precise algorithm based on equiangular division is proposed to make rough paths accurate, allowing SBR to calculate exact channel information. Different ray launching methods are compared to obtain a better launching method. By using equiangular division, rays are launched more uniformly from transmitter (Tx) compared with the current equidistant division method. With the proposed iterative precise algorithm, error of angle of departure (AOD) and angle of arrival (AOA) is below 0.01 degrees. The relationship between the number of iterations and error reduction is also given. It is illustrated that the proposed method has the same accuracy as IM by comparing the power delay profile (PDP) and angle distribution of paths. This can solve the problem of low accuracy brought by SBR.

Orbital angular momentum (OAM) is regarded as a key technology of the sixth generation (6G) wireless communication systems. The vortex wave is a typical application of OAM. In this paper, a novel time-varying three-dimensional (3D) geometry-based stochastic model (GBSM) for a multiple-input multiple-output (MIMO) system employing the vortex wave is proposed. This model supports all kinds of OAM antennas that generate the vortex wave and introduces a spiral phase term to represent the helical wavefront of the vortex wave. To better reveal characteristics of the OAM-based MIMO system, some important statistical properties like temporal autocorrelation function (ACF) and spatial cross-correlation function (CCF) are derived and analyzed. Non-negligible impacts on the temporal ACF caused by OAM antennas are revealed. The results illustrate that the OAM-based MIMO system can help decrease spatial CCF, especially in the environment that angular spreads are small. Characteristics analyzed in this paper are important for future OAM communication systems.

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 novel 3-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed to mimic the ship-to-ship multiple-input multiple-output (MIMO) communication channels. To reflect the realistic maritime propagation environment, the effects of rough sea surface scattering and evaporation duct propagation are investigated in the proposed channel model. The model considers the movements of transmitter (Tx), receiver (Rx), and scatterers, and is capable of capturing the channel characteristics, including non-stationary characteristics, spatial consistency, location-dependent property, etc. Through taking the array cluster evolution into account, the proposed channel model can describe massive MIMO channels and can easily switch to conventional MIMO channel model by adjusting corresponding parameters. Based on the proposed model, key statistical properties like delay/angular/Doppler power spectrum density (PSD), space-time correlation function (STCF), stationary interval, and root mean square (RMS) Doppler/delay spreads in multi-scenarios are derived. The usefulness and accuracy of the proposed model are demonstrated by comparing theoretical results, simulation results, and corresponding measurement results.

The visible light communication (VLC) technology has attracted much attention in the research of the sixth generation (6G) communication systems. In this paper, a novel three dimensional (3D) space-time-frequency non-stationary geometry-based stochastic model (GBSM) is proposed for indoor VLC channels. The proposed VLC GBSM can capture unique indoor VLC channel characteristics such as the space-time-frequency non-stationarity caused by large light-emitting diode (LED) arrays in indoor scenarios, long travelling paths, and large bandwidths of visible light waves, respectively. In addition, the proposed model can support special radiation patterns of LEDs, 3D translational and rotational motions of the optical receiver (Rx), and can be applied to angle diversity receivers (ADRs). Key channel properties are simulated and analyzed, including the space-time-frequency correlation function (STFCF), received power, root mean square (RMS) delay spread, and path loss (PL). Simulation results verify the space-time-frequency non-stationarity in indoor VLC channels. In addition, the influence of light source radiation patterns, receiver rotations, and ADRs on channel characteristics have been investigated. Finally, the accuracy and practicality of the proposed model are validated by comparing the simulation result of channel 3dB bandwidth with the existing measurement data. The proposed channel model will play a supporting role in the design of future 6G VLC systems.

In the military and civilian domains, the modulation classification in the communication system is an extremely important technology that needs to be constantly updated and improved. In this paper, we present an automatic modulation classification (AMC) model to do modulation classification in 5 typical types of signal modulation BPSK, QPSK, 8PSK, 16QAM, and 64QAM. The proposed algorithm uses an improved AlexNet with deep residual learning, regularization, global pooling, and PReLU activation function to extract features from constellation diagrams for better recognition performance. Compared with the original AlexNet, support vector machine (SVM), and the traditional maximum likelihood-based cumulant technique, experiment results indicate that the proposed AMC model with the improved AlexNet has achieved very good recognition results, with its robustness, generalization, and high efficiency. We also explore the identifiability and reliability of signal transmission under different SNR conditions for different modulation types.

In this paper, we conduct wireless channel measurements in indoor corridor scenarios at 2.4, 5 and 6 GHz bands with bandwidth of 320 MHz. The measurement results of channel characteristics at different frequency bands such as average power delay profile (APDP), path loss (PL), delay spread (DS), and Ricean K factor (KF) are presented and analyzed. It is found that the PL exponent (PLE) and PL offset 3 in the floating-intercept (FT) model tend to increase with the increase of frequency. The DS and KF values of the three frequency bands in line of sight (LOS) scenario are basically the same. These results are significant for the design of communication systems.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input-multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space-time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

Sub-1 GHz bands have been used for many years and now some of them will be reallocated for new applications, including the fifth generation (5G) wireless communication systems and beyond, Internet of Things (IoT), smart grid, etc. As the well-known path-loss (PL) models are mainly applicable in 2-6-GHz frequency range, a new channel measurement campaign is needed to study the propagation characteristics at sub-1 GHz bands. In this article, we conduct fixed-to-mobile wideband channel measurements at 400-600-MHz bands in urban and suburban scenarios using the time domain channel sounder. As the interference and noise signals are severe, the transmitted waveform is carefully designed to enlarge the system dynamic range. Meanwhile, ray tracing simulation is applied to construct the measurement environments and do the mutual verification with measurement results. The two-slope PL model and lognormal shadowing fading model are proposed for large-scale fading channel modeling. The root mean square (RMS) delay spread (DS), number of paths, and diffraction characteristics are also analyzed. The results will have great importance for the coming new applications at sub-1 GHz bands.

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.

A novel beam domain channel model (BDCM) for massive multiple-input multiple-output (MIMO) communication systems has been proposed in this paper. The near-field effect and spherical wavefront are firstly assumed in the proposed model, which is different from the conventional BDCM for MIMO based on the far-field effect and plane wavefront assumption. The proposed novel BDCM is the transformation of an existing geometry-based stochastic model (GBSM) from the antenna domain into beam domain. The space-time non-stationarity is also modeled in the novel BDCM. Moreover, the comparison of computational complexity for both models is studied. Based on the numerical analysis, comparison of cluster-level statistical properties between the proposed BDCM and existing GBSM has shown that there exists little difference in the space, time, and frequency correlation properties for two models. Also, based on the simulation, coherence bandwidths of the two models in different scenarios are almost the same. The computational complexity of the novel BDCM is much lower than the existing GBSM. It can be observed that the proposed novel BDCM has similar statistical properties to the existing GBSM at the cluster-level. The proposed BDCM has less complexity and is therefore more convenient for information theory and signal processing research than the conventional GBSMs.

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary massive multiple-input multiple-output (MIMO) channel model for 6G THz indoor communication systems is proposed. In this geometry-based stochastic model (GBSM), the initialization and evolution of parameters in time, space, and frequency domains are developed to generate the complete channel transfer function (CTF). Based on the proposed model, the correlation functions including time auto-correlation function (ACF), spatial cross-correlation function (CCF), and frequency correlation function (FCF) are investigated. The results show that the statistical properties of the simulation model match well with those of the theoretical model. The stationary intervals at different frequencies are simulated. The non-stationarity in time, space, and frequency domains is verified by theoretical derivations and simulations.

Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G). However, the underlying channels are not fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the outdoor, multiple-input multiple-output (MIMO), and vehicle-to-vehicle (V2V) conditions. In this paper, we conduct multi-frequency multi-scenario mmWave MIMO channel measurements with 4 × 4 antennas at 28, 32, and 39 GHz bands for three cases, i.e., the human body and vehicle blockage measurements, outdoor path loss measurements, and V2V measurements. The channel characteristics, including blockage effect, path loss and coverage range, and non-stationarity and spatial consistency, are thoroughly studied. The blockage model, path loss model, and time-varying channel model are proposed for mmWave MIMO channels. The channel measurement and modeling results will be of great importance for further mmWave communication system deployments in indoor hotspot, outdoor, and vehicular network scenarios for B5G.

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.

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.

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.

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.

This paper investigates the blockage loss caused by human body at 11, 16, 28, and 32 GHz by measurements and modeling. The measurements are carried out in an office environment by using a vector network analyzer (VNA) and two horn antennas, with one or two persons walking along or across the line connecting the transmitter (Tx) and receiver (Rx). The METIS knife-edge diffraction (KED) model, Kirchhoff KED model, and geometrical theory of diffraction (GTD) model are used to simulate the human blockage effects. The Gaussian model is also used to fit the measurement data. The human blockage effects are compared for the four millimeter wave (mmWave) bands. The results have shown that as the frequency increases, there is no obvious increasing trend of the losses. The METIS KED model, Kirchhoff KED model, and G TD model can simulate the human blockage effects well.

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.