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.

Imagery from the thermal infrared spectrometer (TIS) of the first sustainable development goals science Satellite (SDGSAT-1) provides a high-resolution observation view of ground objects, while the nonlinear response between imaging modules affects the quality of the image. Using the thermal infrared data from SDGSAT-1 TIS, an effective method of intermodule nonlinear response correction is presented in this letter. First, an improved frequency domain guided LRSID algorithm is utilized to eliminate the fringe noise in the module, in conjunction with a boundary condition to avoid distortion at the boundary of the module. Next, a nonlinear response correction method utilizing a fully connected neural networks is developed to compensate for the difference between modules based on overlapping pixels. The correction parameters are calculated by the network trained on overlapping pixels from noiseless images. Results show that this method has produced considerable improvements in visual effects, and the distortion in response between modules was controlled below 0.5%, which is comparable with the work for Landsat 8. Besides, the edge slope (ES) of the corrected image makes almost no reduction, which means the nonlinear response is corrected without sacrificing image quality.

In the on-orbit images of the SDGSAT-1 TIS, there are image striping phenomena caused by the nonlinear response of the detector and flickering effects caused by the detector’s jump response. Based on the characteristics of the SDGSAT-1 TIS, a detection method for anomalous pixels using time-domain gradients and nonlinearity is proposed. This method also includes adaptive threshold adjustment and on-orbit data detection based on scene images. The results show that in each band, the number of flickering pixels with a detection probability exceeding 95% is fewer than 7, and the relative positions of these flickering pixels in the images are fixed. Additionally, traceability analysis of the on-orbit radiometric data reveals that the noise deviation of flickering pixels is more than 10% greater compared to normal pixels. This indicates that the flickering behavior is not random and that these pixels are more prone to flickering than others. In each band, the number of nonlinear-response pixels with a detection probability exceeding 95% is fewer than 6. Additionally, a traceability analysis of on-orbit radiometric data shows that the response deviation of nonlinear-response pixels is more than 0.7% compared to normal pixels. This result indicates that the occurrence of nonlinear-response pixels on orbit is not random and provides a new approach for early detection of anomalous pixels based on on-orbit radiometric data.

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

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

Emotion recognition has become a research focus in the brain–computer interface and cognitive neuroscience. Electroencephalogram (EEG) is employed for its advantages as accurate, objective, and noninvasive nature. However, many existing research only focus on extracting the time and frequency domain features of the EEG signals while failing to utilize the dynamic temporal changes and the positional relationships between different electrode channels. To fill this gap, we develop the dynamic differential entropy and brain connectivity features based EEG emotion recognition using linear graph convolutional network named DDELGCN. First, the dynamic differential entropy feature which represents the frequency domain feature as well as time domain feature is extracted based on the traditional differential entropy feature. Second, brain connectivity matrices are constructed by calculating the Pearson correlation coefficient, phase-locked value and transfer entropy, and then are used to denote the connectivity features of all electrode combinations. Finally, a linear graph convolutional network is customized and applied to aggregate the features from total electrode combinations and then classifies the emotional states, which consists of five layers, namely, an input layer, two linear graph convolutional layers, a fully connected layer, and a softmax layer. Extensive experiments show that the accuracies in the valence and arousal dimensions reach 90.88% and 91.13%, and the precision reaches 96.66% and 97.02% on the DEAP dataset, respectively. On the SEED dataset, the accuracy and precision reach 91.56% and 97.38%, respectively.

Emotion recognition is a key technique of intelligent human-computer interaction (HCI) systems. In the current research on emotion recognition, there are several limitations such as inconsistent brain network scale and high time complexity of modal decomposition. To overcome these shortcomings, we propose a novel emotion recognition method incorporating MST-based brain network and FVMD-GAMPE. Firstly, electroencephalography (EEG) data is decomposed into four frequency bands $(\theta,\alpha,\beta,\gamma)$ by wavelet packet transform (WPT), and mutual information (MI) between channel pairs is calculated to construct the connectivity matrix. Secondly, the brain network based on the minimum spanning tree (MST) is constructed and seven features are extracted. Thirdly, fast variational modal decomposition (FVMD) and WPT are applied to process EEG data to obtain the variational mode functions (VMF) of different frequency bands. Then, the parameters of the multi-scale permutation entropy (MPE) are optimized with the genetic algorithm (GA), and then MPE features are extracted. Finally, the features extracted from MST-based brain network are fused with MPE features, and then fused features are fed to the random forest (RF) classifier to recognize emotional states. Experimental results on DEAP show that the best classification accuracy for valance and arousal are 89.58% and 88.54%, respectively. The result analysis demonstrates MST-based brain network in the negative emotional states has a more divergent topology. This means that brain regions are more active and have a faster exchange of information flow when the brain processes negative emotions. On the other hand, brain network of women is similar to a star-shaped structure, which indicates women’s brain activation is higher than man. This study provides theoretical support for research on negative bias.

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.

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

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.

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.

Most channel models in the literature are based on the assumption that the mobile station (MS) moves along a straight line with a constant speed. In a realistic environment, the MS may experience changes in their speeds and trajectories. In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model allowing for velocity variations of the MS is proposed. The parameters are obtained from the WINNER+ channel model to make the simulations more realistic. Statistical properties including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), and Doppler power spectral density (PSD) are derived and analyzed. Our findings show that a variation of the velocity of the MS has a significant impact on the statistical properties of the channel model. Furthermore, the proposed channel model can be used as a basic framework for future non-stationary channel modeling.