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.

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.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) provides an almost uniformly high data rate for all user equipment (UE) through multiple access points (APs), with a non-negligible signal processing burden. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing$a$priori channel information, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for uplink CF M-MIMO communications. First, we propose a twostage data reception and pilot assignment scheme constituting receive combining and large-scale fading decoding (LSFD) to reduce overall interference and maximize spectral efficiency (SE). Furthermore, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived SE upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed schemes outperform both their space domain alternatives and those without using channel maps in terms of SE.

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

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.

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.

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

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a decisive role in the design, optimization, and evaluation of communication systems between UAV and ground terminal. This paper proposes a three-dimensional (3D) model for U2G communication channels, enhanced with ultra-wideband (UWB) features and frequency non-stationarity. This model integrates large-scale and small-scale fading components, introducing bandwidth-dependent path numbers and the UAV posture matrix for realistic scenario representation. It encompasses specific UWB U2G channel phenomena such as the channel hardening, UAV 3D movements, and posture variation effect. The channel parameters, including spatial large-scale parameters (LSPs), bandwidth-correlated path numbers, delay-posture-correlated path power, and frequency-correlated path phase, are generated to capture channel non-stationary characteristics across time and frequency domains. Employing ray-tracing (RT) for the path number and optimization methods for the path delay, the proposed model ensures reliable parameter evolution. The proposed model is assessed through key statistical properties, including space-time-frequency correlation functions, power delay profile, root-mean-square delay spread, Doppler power spectrum density, and the energy variance. It is demonstrated that both posture and bandwidth variations have crucial effects on channel characteristics. The validity and practicability of this research is demonstrated by comparing the simulated outcomes with the measurement data.

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.

In this paper, a quasi-deterministic (Q-D) model for non-stationary underwater acoustic (UWA) channels is proposed. This model combines the BELLHOP deterministic model and geometry-based stochastic model (GBSM), which provides higher accuracy and flexibility. Different propagation components in shallow water are classified as D-rays, R-rays and F-rays in the proposed model, where D-rays are modeled by BELLHOP while both R-rays and F-rays are modeled by GBSM. Some important channel statistical properties, including time-frequency correlation function (TF-CF), Doppler power spectrum density (PSD), average Doppler shift, and RMS Doppler spread are derived and simulated. Finally, simulation results illustrate the correctness of the proposed model.

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

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.

Underground space is relative to above-ground space, mainly including underground shopping malls, underground parking lots, underground mines, etc. This paper focuses on the characteristics of wireless transmission in underground mines. In this paper, a new hybrid multimode waveguide model is proposed. Considering the movement of the receiving antenna, the proposed model combines the roughness loss and tilt loss of walls, and provides an analytical expression for the received power at any position in a tunnel. Using the proposed model, various tunnel factors are analyzed, such as the operating frequency, the cross section size, etc. It is found that, in underground mine, the curve of signal power can be divided into near field and far field. The operating frequency and the cross section size can affect the received signal power significantly. For different transmitting antenna positions, the same change of the receiving antenna position has different effects. The dielectric constant has little effect on received signal power.

In this paper, we consider several kinds of fading channels which are used to describe the multipath effect, including Rayleigh, Rice, Nakagami, Nakagami-Hoyt, and $\kappa - \mu$. The communication performance indicators for each kind of channel, such as instantaneous signal-to-noise rate (SNR), outage probability (OP), and average error rate are analyzed and some novel expressions are derived. Among them, the OP is derived by calculating the cumulative distribution function (CDF) of the instantaneous SNR, and the average error rate is derived from the probability density function (PDF) of the instantaneous SNR with integral operation, by using the direct method or the moment generating function method. The most obvious result is that the increase of channel parameters $m, \kappa, \mu$, and q leads to lower OP and lower average error rate. Moreover, the same results can be obtained by taking the same special case of different generalized fading channels.

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.

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.

In the next-generation wireless networks, unmanned aerial vehicles (UAVs) equipped with aerial base stations (ABSs) can serve as the supplement of traditional terrestrial networks and provide high-speed access for ground users. In this paper, an extended multi-user massive multiple-input multiple output (MIMO) geometry-based stochastic channel model (GBSM) is proposed for UAV-aided communication systems. The proposed model is the extension of a non-stationary UAV channel model by taking the different antenna array layouts and spatial correlation between ground users into account. Based on the simulation, the effects of UAV height, user density, and antenna configuration on inter-user correlation and on channel capacity are thoroughly investigated. The extension of spatial correlation will enable UAV channel simulator to generate realistic spatial correlated channel impulse responses (CIRs) for multiple users that are relatively close to one another.

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.

To achieve space-air-ground-sea integrated communication networks for future sixth generation (6G) communications, unmanned aerial vehicle (UAV) communications applying to maritime scenarios serving as mobile base stations have recently attracted more attentions. The UAV-to-ship channel modeling is the fundamental for the system design, testing, and performance evaluation of UAV communication systems in maritime scenarios. In this paper, a novel non-stationary multi-mobility UAV-to-ship channel model is proposed, consisting of three kinds of components, i.e., the line-of-sight (LoS) component, the single-bounce (SB) components resulting from the fluctuation of sea water, and multi-bounce (MB) components introduced by the waveguide effect over the sea surface. In the proposed model, the UAV as the transmitter (Tx), the ship as the receiver (Rx), and the clusters between the Tx and Rx, can be seen as moving with arbitrary velocities and arbitrary directions. Then, some typical statistical properties of the proposed UAV-to-ship channel model, including the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectrum density (PSD), delay PSD, angular PSD, stationary interval, and root mean square (RMS) delay spread, are derived and investigated. Finally, by comparing with the available measurement data, the accuracy of proposed channel model is validated.

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.

Air-to-ground (A2G) communication plays an important role in ensuring reliable communication links between unmanned aerial vehicles (UAVs) and ground terminals. This paper presents a wideband truncated ellipsoidal shaped scattering region (TESR) geometry based stochastic model (GBSM) for Multiple-Input-Multiple-Output (MIMO) A2G channels. The proposed model contains a line-of-sight (LoS) component, a ground reflection component, and truncated ellipsoid scattering components. Based on the proposed GBSM, some important statistical properties like space-time-correlation-function (STCF) and Doppler power spectrum density (PSD) are derived. The impacts of elevation angle and UAV altitude on A2G channel characteristics are analyzed. Finally, excellent agreement is achieved between measurement data and simulation results of temporal auto correlation functions (ACFs), demonstrating applicability of the proposed model.