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.

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.

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.

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.

Massive multiple-input multiple-output (MIMO) channels are distinctly characterized by their array non-stationarity, which has not been considered in the existing beam domain channel models (BDCMs). In this paper, the array non-stationarity of massive MIMO channels is modeled by the spatially consistent visibility regions (VRs) over a large uniform planar array (UPA) in terms of individual multipath components (MPCs). Based on this, a novel three-dimensional (3D) BDCM incorporating the effects of array non-stationarity is proposed. Statistical properties of the proposed BDCM including channel power, power leakage, space-time-frequency correlation function (STF-CF), and beam spread are derived. The ergodic and outage capacities are evaluated. The impacts of array non-stationarity on those statistics and channel capacity are analyzed. Results suggest that the beamwidths or spatial resolutions of the BDCM for different directions are not equal due to the array non-stationarity. This in turn increases the power leakage and correlation between channel elements and reduces the beam domain channel capacity.

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.

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.

High-speed train (HST) communications in tunnels have attracted more and more research interests recently, especially within the framework of the fifth generation (5G) wireless networks. In this paper, based on cuboid-shape, three-dimensional (3D) non-stationary wideband geometry-based stochastic models (GBSMs) for HST tunnel scenarios are proposed. By considering the influence of the tunnel walls, a theoretical channel model is first established, which assumes clusters with an infinite number of scatterers randomly distributed on the tunnel walls. The corresponding simulation model is then developed and the method of equal areas is employed to obtain the discrete parameters, such as the azimuth and elevation angles. We derive and investigate the most important channel statistical properties of the proposed 3D GBSMs, including the time-variant autocorrelation function, spatial cross-correlation function, and Doppler power spectrum density. It is indicated that all statistical properties of the simulation model, verified by simulation results, can match very well with those of the theoretical model. Furthermore, a validation is presented by comparing the stationary regions of our proposed tunnel channel model to those of relevant measurement data.

This paper proposes 3D non-stationary multipleinput multiple-output (MIMO) geometry-based stochastic models (GBSMs) for high-speed train (HST) tunnel channels. Considering the line-of-sight (LoS), single-bounced (SB), and double-bounced (DB) components from the geometrical tunnel scattering model, a reference HST tunnel channel model under the assumption that scatterers are uniformly distributed on the tunnel walls is first derived. Then, by using the modified method of equal areas (MMEA), the corresponding simulation model is developed. Based on the proposed tunnel channel models, the correlation properties in time and space domains are investigated. A good agreement of statistical properties between the reference model and simulation model can be obtained. Furthermore, the simulation results show that the proposed model can be applied to mimic the nonstationarity of HST tunnel channels.

In this paper, we investigate the impact of different parameters on channel characteristics in a high-speed train (HST) ray tracing tunnel channel model. Signal propagation in HST tunnel scenarios differs much from that of other HST scenarios due to the unique construction of tunnels. Ray-tracing method is applied to analyze the received power and power delay profile (PDP) of tunnel channel models. Different parameters, i.e., carrier frequency, tunnel shape, tunnel dimension, the distance between the transmitter (Tx) and receiver (Rx), and antenna polarization, are studied via simulation results.

In this paper, we compare the statistical properties of high-speed train (HST) wireless channels in different scenarios using a generic non-stationary HST channel model that has been verified by channel measurements (Ghazal et al., 2015). We mainly focus our comparison and analysis on the three most common HST scenarios, i.e., the rural area, cutting, and viaduct scenarios. Several channel statistical properties such as the temporal autocorrelation function (ACF), space cross-correlation function (CCF), and space- Doppler (SD) power spectrum density (PSD) are investigated. The impacts of different scenario- specific parameters on the channel statistical properties are also studied via numerical analysis.