With the rise of low-altitude economy applications, 6G communication systems place stricter demands on optical fiber amplifiers, requiring wider bandwidth, higher gain, and better spectral uniformity. Backbone networks for low-altitude integrated sensing and communication systems, in particular, call for high-performance amplification to support robust data transmission and reliable sensing. However, traditional multi-pump Raman fiber amplifiers (RFAs) are no longer adequate for meeting the performance demands of distributed fiber optic sensing networks in such scenarios. To address this problem, this paper proposes a machine learning-enhanced multi-pump RFA for high-performance optical backbone in low-altitude sensing and communication. The back propagation neural network (BPNN) is employed to accurately model the nonlinear relationship between pump parameters and amplification performance, facilitating adaptive and fine-grained control over signal gain, which is critical for maintaining stable and efficient data transmission across dynamic and heterogeneous communication scenarios. Moreover, the artificial bee colony (ABC) algorithm is integrated to perform global optimization of pump wavelengths and power configurations, thereby improving overall system bandwidth, gain characteristics, and operational robustness under diverse and unpredictable network conditions. The experimental results demonstrate that the proposed method achieves superior prediction accuracy, enhanced stability, and greater adaptability compared to conventional algorithms.

Extensive channel measurement campaigns have shown that multipath components (MPCs) generally exhibit clustered distributions, making cluster-based models a cornerstone of wireless channel modeling. Developing time-varying cluster-based channel models requires not only clustering MPCs in the delay, angle, and power domains, but also tracking their temporal evolution. However, existing multipath clustering algorithms typically rely on fixed hyperparameters and therefore are often poorly suited to dynamically evolving clusters, while tracking algorithms based solely on distance metrics are prone to ambiguous associations among spatially proximate clusters. Moreover, treating clustering and tracking as separate processes often prevents temporal evolution information from being fully exploited during clustering. This paper proposes a structure-aware unified framework that integrates Adaptive Neighborhood Robust Mean Shift (AN-RMS) clustering with BoxKF-Cluster tracking. Within the proposed framework, AN-RMS, built upon kernel density estimation, adaptively determines the effective number of nearest neighborsKby detecting abrupt changes in the second-order gradient of the neighborhood-distance sequence, thereby identifying structural cluster boundaries and providing locally adaptive guidance for density-mode iteration. BoxKF-Cluster introduces oriented bounding boxes derived from root-mean-square statistics to characterize the evolving morphology of clusters, and combines Kalman filtering with a successive-interference-cancellation (SIC)-like strategy, in which the influence of existing clusters is first removed to facilitate the detection of newly emerging ones. As a result, clustering and tracking are jointly accomplished within a unified framework. The proposed method is validated in complex scattering scenarios using both ray-tracing simulations and real vehicle-to-vehicle millimeter-wave measurement data, demonstrating its effectiveness for time-varying channel modeling.

High reliability and low latency are essential to railway wireless communications, which transmit train control and dispatch commands to ensure operational safety. However, as railway systems become increasingly electrified and more complex, the exposure to electromagnetic interference (EMI) also grows, potentially causing service disruptions and compromising safety. Intentional EMI (IEMI), which is deliberately and often maliciously generated, further increases the vulnerability of these critical communication networks. Real-time detection and classification of EMI and IEMI therefore become increasingly important. This paper presents composite models that reflect realistic railway scenarios and proposes an adaptive classification approach for EMI and IEMI using a deep learning algorithm based on bidirectional long-short-term memory (BiLSTM) networks and attention mechanisms. By employing time-series feature extraction to analyze both time and frequency information at fine resolution, the proposed method demonstrates a classification accuracy of 94.98%. Simulation results outperform existing techniques with a 3% improvement in accuracy, showcasing its adaptability across four typical railway scenarios at train speeds of up to 500 km/h. Moreover, online monitoring phase performs real-time detection in just 7.43 ms, meeting the stringent latency requirements for railway systems. Validation using real-world data further confirms the practical applicability of the proposed methods under actual operating conditions.

Integrated Sensing and Communication (ISAC) technology plays a critical role in future intelligent transportation systems, by enabling vehicles to perceive and reconstruct the surrounding environment through reuse of wireless signals, thereby reducing or even eliminating the need for additional sensors such as LiDAR or radar. However, existing ISAC-based reconstruction methods often lack the ability to track dynamic scenes with sufficient accuracy and temporal consistency, limiting the real-world applicability. To address this limitation, we propose a deep learning based framework for vehicular environment reconstruction by using ISAC channels. We first establish a joint channel–environment dataset based on multi-modal measurements from real-world urban street scenarios. Then, a multi-stage deep learning network is developed to reconstruct the environment. Specifically, a scene decoder identifies the environmental semantic context such as buildings, trees, and so on; a semantic center decoder predicts coarse spatial layouts by localizing dominant object centers; a point cloud decoder recovers fine-grained geometry and structure of surrounding environments. Experimental results demonstrate that the proposed method achieves high-quality global reconstruction of dynamic environments, with a Chamfer Distance of 0.29 and [email protected] of 0.87. In addition, complexity analysis demonstrates the efficiency and practical applicability of the method in real-time scenarios. This work provides a pathway toward low-cost environment reconstruction based on ISAC for future intelligent transportation.

As a novel technology in the sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) enables intelligent agents to perceive, predict, and interact with the environment. It is crucial for ISAC to acquire environmental information based on electromagnetic propagation, referred to as channel semantics, to facilitate tasks such as decision-making and beamforming. However, channel models that focus on physical characteristics face challenges in representing the semantics embedded in the channel, thereby limiting the performance evaluation of ISAC systems. To tackle this, we present a novel unified framework for channel modeling from the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into composable and extensible high-level semantic characterization, thereby better capturing the relationship between the environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics from three levels: status semantics, behavior semantics, and event semantics, corresponding to channel transient multipaths, channel time-varying trajectories, and channel topology, respectively. Taking a realistic vehicular ISAC scenario as an example, we perform semantic clustering through depth estimation and semantic segmentation of environmental images, and further characterize the channel status semantics by fitting multipath statistical distributions; behavior semantics are modeled using Markov chains to capture time-varying characteristics; event semantics are characterized by employing a co-occurrence matrix. The results indicate that the proposed model can generate accurate channels whereas representing rich semantic information. Additionally, generalization of the model for customized semantics is demonstrated.

As next-generation wireless communication systems need to be able to operate in high-frequency bands and high-mobility scenarios, delay-Doppler (DD) domain multicarrier (DDMC) modulation schemes, such as orthogonal time frequency space (OTFS), demonstrate superior reliability over orthogonal frequency division multiplexing (OFDM). Accurate DD domain channel modeling is essential for DDMC system design. However, since traditional channel modeling approaches are mainly confined to time, frequency, and space domains, the principles of DD domain channel modeling remain poorly studied. To address this issue, we propose a systematic DD domain channel measurement and modeling methodology in high-speed railway (HSR) scenarios. First, we design a DD domain channel measurement method based on the long-term evolution for railway (LTE-R) system. Second, for DD domain channel modeling, we investigate quasi-stationary interval, statistical power modeling of multipath components, and particularly, the quasi-invariant intervals of DD domain channel fading coefficients. Third, via LTE-R measurements at 371 km/h, taking the quasi-stationary interval as the decision criterion, we establish DD domain channel models under different channel time-varying conditions in HSR scenarios. Fourth, the accuracy of proposed DD domain channel models is validated via bit error rate comparison of OTFS transmission. In addition, simulation verifies that in HSR scenario, the quasi-invariant interval of DD domain channel fading coefficient is on millisecond (ms) order of magnitude, which is much smaller than the quasi-stationary interval length on 100 ms order of magnitude. This study could provide theoretical guidance for DD domain modeling in high-mobility environments, supporting future DDMC and integrated sensing and communication designs for 6G and beyond.

5G-for-Railway (5G-R), the next-generation railway dedicated mobile communication system, has a leap-forward improvement compared with the previous system. Accompanied by the differences in frequency, bandwidth and scenario, it brings new challenges to channel characterization and modeling. This paper presents our latest research in this field. Specifically, the 5G-R professional channel measurement of a real railway scenario is carried out. Based on the measured data, the multidimensional channel characteristics such as large-scale fading, delay spread, stationarity, and spatial distribution are evaluated. Furthermore, key cluster parameters such as lifetime, cluster power, delay, and their correlation are analyzed to support the mainstream cluster-based channel model. These pioneering measurement and modeling work in this field can provide a basis for forming reliable and accurate 5G-R channel models.

In the high-speed railway (HSR) scenario, handover (HO) reliability is constrained by the high-speed movement and weak coverage at cell edges, posing a threat to the "always online" transmission requirement. To address this, we propose a dynamic beamforming scheme to mitigate HO failures and improve data rates. First, a beamforming-based HO model is established, quantifying the impact of beam direction on data rate and HO reliability. Based on this, an optimization problem is formulated, aiming to enhance data rate, reduce HO failure probability, and minimize beam adjustment overhead. Subsequently, a dynamic beam adjustment algorithm based on deep reinforcement learning is designed, leveraging its real-time decision-making capability to adaptively optimize the beam direction under rapidly changing channel conditions. Simulation results demonstrate that the proposed scheme requires only 23% of the beam adjustment overhead of the ideal real-time precise beamforming, while achieves nearly identical HO performance and 98.9% of its data rate.

Dynamic wireless channel modeling is essential for future communication system design. However, existing methods struggle to capture the long-term non-stationary behavior of real-world channels driven by high mobility, dense deployment, and complex environments in the sixth generation (6G) scenarios. As system-level design depends on channel statistics rather than exact realizations, statistically consistent channel generation offers a more appropriate modeling strategy. In this work, We propose a deep learning-based hybrid framework that shifts the modeling goal from precise point-wise prediction to generating synthetic channel sequences that statistically replicate long-term non-stationary dynamics. Instead of forecasting exact future states, the model reproduces key statistical features such as the evolution of power delay profiles (PDPs), root mean square (RMS) delay spread and wide-sense stationary (WSS) regions. Experimental results demonstrate that the generated sequences closely match reference data, supporting scalable data generation for digital twins and system-level simulations under realistic dynamic conditions.

As the demand for ubiquitous connectivity and environmental awareness continues to rise in the 6G era, Integrated Sensing and Communication (ISAC) technology has become a key pathway to realizing high-precision sensing and intelligent awareness services. To explore the application potential of ISAC in passive target localization, we build a passive bistatic sensing system on a 26 GHz millimeter-wave communication platform and conduct measurement experiments in an open square scenario. The system estimates multipath propagation parameters from the received sensing channel and, combined with a power-weighted clustering method, extracts multipath features to jointly localize static scatterers and moving targets. Experimental results show that, without requiring any involvement of the target device, the system attains sub-meter passive localization accuracy. The study validates the feasibility and practical value of millimeter-wave ISAC for high-precision sensing and localization in complex environments.

Accurate path loss prediction is crucial to evaluation of wireless coverage, and deep learning based path loss prediction has recently received a lot of attention in complex scenarios. Previous studies based on deep learning methods often overlook impacts of radio propagation. Therefore, this paper proposes a deep learning framework enhanced by empirical propagation models, which guides the network learning process through physical constraints to enhance the prediction model. The model uses a convolutional neural network to extract environmental features and analyzes impacts of different empirical models at various network locations on path loss prediction performance. Experimental results show that the deep learning network using the close-in free space reference distance path loss model achieves fairly high prediction accuracy on a 5.9 GHz urban scenario dataset, with a root mean square error as low as 5.63 dB.

ABSTRACT With the evolution of wireless communication technologies towards the sixth generation (6G) mobile communication system, the space‐air‐ground‐sea integrated network architecture has emerged as a critical development direction for achieving global seamless coverage. Focusing on the unmanned aerial vehicle (UAV)‐to‐ship maritime communication scenario within this network framework, a three‐dimensional (3D) geometry‐based stochastic model is proposed. The model adopts a combined structure of elliptical and cylindrical components to comprehensively characterize multipath propagation mechanisms, including line‐of‐sight, sea surface reflection, as well as single‐bounced and double‐bounced components. By introducing the wave equation of sea surface to establish the 3D motion trajectory model of the ship and integrating it with the 3D rotational motion model of the UAV, the time‐varying propagation distance‐induced channel non‐stationarity is accurately captured. Based on this model, key statistical characteristics such as the space‐time‐frequency correlation function (STF‐CF) and Doppler power spectral density are derived. Furthermore, the impacts of sea surface wind speed, UAV rotation, ship oscillation, and ship size on channel statistical properties and space‐time non‐stationarity are thoroughly analysed. These numerical results provide theoretical foundations for the design and performance optimization of UAV‐assisted communication systems in complex maritime environments.

Ray tracing (RT) is a key tool for establishing accurate mappings between physical environments and propagation channels. However, due to the complexity of indoor scatterers and the difficulty of fully capturing modeling details, there is no unified specification for scenario modeling, leaving the impact of modeling detail on RT simulations unclear. This work proposes an indoor modeling method based on LiDAR point clouds, which achieves automated mesh reconstruction via minimum bounding box estimation and generates scenario models at different levels of detail (LOD). Comparative analysis shows that modeling detail significantly affects multipath components (MPCs) accuracy. A simple-detail model fails to accurately capture MPCs, whereas a medium-detail model exhibits higher simulation accuracy, and the full-detail model achieves the closest agreement with the ground truth model, although further accuracy gains diminish as complexity increases. Furthermore, indoor scatterers detail has a greater influence on RT results than room boundaries. To balance simulation accuracy and computational complexity, indoor scatterers should include at least external contour features, with opening structures preferred. In contrast, RB modeling can be simplified to a medium-detail model with only external contours. The sensitivity of different channel parameters to LOD also depends on the propagation scenario: in LOS scenarios, DS and PL are highly sensitive to LOD, whereas AS is relatively insensitive; in NLOS scenarios, DS and AS are sensitive, while PL sensitivity significantly decreases.

In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

Integrated Sensing and Communication (ISAC) is a promising technology for 6G, with the goal of providing end-to-end information processing and inherent perception capabilities for future communication systems. Within ISAC emerging application scenarios, vehicular ISAC technologies have the potential to enhance traffic efficiency and safety through integration of communication and synchronized perception abilities. To establish a foundational theoretical support for vehicular ISAC system design and standardization, it is necessary to conduct channel measurements, and model to obtain a deep understanding of the radio propagation. In this paper, a dynamic statistical channel model is proposed for vehicular ISAC scenarios, incorporating Sensing Multi-Path Components (S-MPCs) and Clutter Multi-Path Components (C-MPCs), which are identified by the proposed tracking algorithm. Based on actual vehicular ISAC channel measurements at 28 GHz, time-varying sensing characteristics in front, left, and right directions are investigated. To model the dynamic evolution process of channel, number of new S-MPCs, lifetimes, initial power and delay positions, dynamic variations within their lifetimes, clustering, power decay, and fading of C-MPCs are statistically characterized. Finally, the paper provides implementation of dynamic vehicular ISAC model and validates it by comparing key simulation statistics between measurements and simulations.

Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer. Aiming to the vision of sixth generation (6G) networks, wireless devices are expected to have native perception and intelligent capabilities, which associate wireless channel with surrounding environments from physical propagation dimension to semantic information dimension. Inspired by these, we aim to provide a new paradigm on wireless channel from semantic level. A channel semantic model and its characterization framework are proposed in this paper. Specifically, a channel semantic model composes of status semantics, behavior semantics and event semantics. Based on actual channel measurement at 28 GHz, as well as multi-mode data, example results of channel semantic characterization are provided and analyzed, which exhibits reasonable and interpretable semantic information.

The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.

Wireless channel modeling is widely considered as foundation of wireless communication system design. Sufficient and diverse channel data provides strong support for wireless channel characterization and modeling. However, channel data from real measurement is usually limited considering complexity of channel measurements for different scenarios and frequency bands. In this work, a deep learning-based cross-frequency channel generation and modeling framework is proposed. Without requiring a traditional parametric channel model, the proposed framework can generate realistic cross-frequency channels by employing generative adversarial networks. Based on vehicular channel measurement data, cross-frequency reconstruction performance of the proposed framework is validated by comparing characteristics of measured and reconstructed channels. It is also found that channel non-stationary characteristics can be well embodied in the reconstructed channels.

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Different from the traditional terrestrial intelligent reflecting surface (IRS), aerial IRS (AIRS) can provide some unique advantages, such as flexible deployment and wider-view signal reflection. In this paper, a three-dimensional (3D) single cylinder simulation channel model is proposed for AIRS-aided multiple-input multiple-output (MIMO) communication systems, where the considered propagation scenario consists of a fixed base station (BS) and a mobile station (MS). Based on the model, the channel impulse response (CIR), spreading function, and channel capacity are derived. Then, some heuristic algorithms are proposed to obtain the phase shifts of the IRS elements. It is found that multipath fading and Doppler effects stemming from the movement of MS can be effectively mitigated via adjusting the tunable phase shifts of the IRS elements. Moreover, the channel capacity of the system could also be improved by the proposed schemes. These findings can be used to lay a foundation for developing intelligent and controllable propagation environments.

Unmanned aerial vehicle (UAV) communications are expected to play a major role in future space-air-ground integrated networks (SAGINs). In this paper, a geometric three-dimensional (3D) non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered. Unlike the existing UAV-based GBSMs, the proposed model considers both local and far clusters in the propagation environments. On this basis, a continuous-time Markov model with two states is used to model the dynamic properties of clusters (i.e., clusters appear/disappear with time), and the closed-form expressions of the survival probabilities of clusters are derived. Furthermore, we derive and investigate some significant statistical properties, including space-time-frequency correlation function, quasi-stationary interval, and Doppler power spectrum. Numerical results show that the local mobile cluster (LMC) component leads to higher time correlation compared with the local stationary cluster (LSC) component. In addition, it is found that the LMC component leads to larger quasi-stationary interval compared with the LSC component. Finally, it is found that the motion of transceivers and clusters, and the changes of carrier frequency introduce significant fluctuations in Doppler power spectrum. These observations and conclusions can be considered as a guidance for mmWave UAV MIMO system design.

Orthogonal time frequency space (OTFS) modulation has been widely considered for high-mobility scenarios. Satellite-to-ground communications have recently received much attention as a typical high-mobility scenario and face great challenges due to the high Doppler shift. To enable reliable communications and high spectral efficiency in satellite mobile communications, we evaluate OTFS modulation performance for geostationary Earth orbit and low Earth orbit satellite-to-ground channels at sub-6-GHz and millimeter-wave bands in both line-of-sight and non-line-of-sight cases. The minimum mean squared error with successive detection (MMSE-SD) is used to improve the bit error rate performance. The adaptability of OTFS and the signal detection technologies in satellite-to-ground channels are analyzed. Simulation results confirm the feasibility of applying OTFS modulation to satellite-to-ground communications with high mobility. Because full diversity in the delay-Doppler domain can be explored, different terminal movement velocities do not have a significant impact on the performance of OTFS modulation, and OTFS modulation can achieve better performance compared with classical orthogonal frequency division multiplexing in satellite-to-ground channels. It is found that MMSE-SD can improve the performance of OTFS modulation compared with an MMSE equalizer.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) technology can provide high data rate for the fifth generation (5G) and beyond wireless networks. In this paper, a non-stationary geometric mmWave multiple-input multiple-output (MIMO) channel model is proposed for air-to-air (A2A) communications based on a three-dimensional (3D) cylinder model. To describe the real A2A propagation environments, a two-state continuous-time Markov process is introduced to model the dynamic properties (appearance and disappearance) of scatterers. The movements of the transmitter and receiver result in time-varying angles and propagation distances that make the model more general. Based on the proposed model, the space-time-frequency correlation functions are derived using the survival probabilities of scatterers. The effects of some important model parameters on channel correlation and non-stationarity are investigated. The observations and conclusions can be used to evaluate and optimize the performance of mmWave UAV A2A communication systems.

Unmanned aerial vehicle (UAV) communications are envisioned to support numerous applications in the fifth and sixth generations wireless networks. In this paper, a three-dimensional (3D) non-stationary semi-spherical geometrical model is proposed for narrowband multi-input multi-output (MIMO) UAV channels. The Gauss-Markov mobility model is used to characterize the UAV rotation for the first time, which results in time-varying elevation and orientation angles of the antenna array and further leads to channel non-stationarity. Based on the proposed model, the space-time correlation function is derived and investigated in terms of the UAV movements (including pitch, roll, and heave). These observations and conclusions can be used as a reference for the system design and performance analysis of UAV-MIMO communication systems.

Vehicle obstruction is a special scenario in vehicle-to-vehicle (V2V) communications. In this case, channel characteristics including path loss and spatial distributions are obviously different from other typical vehicular communication scenarios. However, the vehicle obstruction scenario is difficult to be identified by global navigation satellite systems (GNSS) or radars, so it is difficult for V2V communication systems to respond to sudden changes in channel characteristics due to vehicle obstructions. Therefore, by correctly identifying the vehicle obstruction scenarios, V2V communication systems can select appropriate propagation channel models to maintain an effective and reliable operating state. For this reason, this paper presents a machine-learning-based vehicle obstruction scenario identification approach for V2V communications. Channel characteristics extracted from measurements form the datasets used to training, then the back-propagation neural network (BPNN) is trained and a scenario identification model is obtained. Subsequently, identification accuracy is verified by using validation data. The results show that the identification accuracy for vehicle obstruction scenarios is more than 97%, which indicates that the approach proposed in this paper shows good performance in vehicle obstruction scenario identification in V2V communications.

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

For radio propagation, the wireless channel generally changes more significantly in the non-line-of-sight (NLOS) scenario compared to the line-of-sight (LOS) scenario. For vehicle-to-vehicle (V2V) communications, the most typical NLOS scene is large vehicle obstructions. Therefore, in order to understand the radio propagation mechanism and channel characteristics in V2V communication, it is necessary to carry out indepth investigations on V2V channel under the condition of large vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions in the urban environment at 5.9 GHz are carried out. Based on the measured data, extraction and analysis of channel parameters are performed. Specifically, this paper focuses on the directional analysis of V2V channel in the case of large vehicle obstructions. It can be find that the occlusion of large vehicles not only causes additional attenuation of signal energy, but also significantly affects the angular distribution of multipath components in three- dimensional space, and brings larger angular dispersion. These results would be useful for establishing a more accurate channel model and serveing the design of V2V communication system.

Multi-link transmission is one of the promising communication techniques capable of improving system capacity and cell-edge user spectral efficiency in railway communication networks. The performance of multi-link transmission heavily depends on the propagation characteristics of radio channels, especially the correlation property between multiple radio channels. Thus, it is important to characterize the multi-link channel in railway communication networks. In this paper, extensive wideband measurements in a viaduct railway environment at 460 MHz with two base stations and one mobile station are performed. Large-scale parameters (LSPs), including large-scale fading, Ricean K-factor, delay spread, and angle spread, are extracted from the measurement data. Based on the measurements, auto-correlation and cross-correlation properties of each LSP are investigated. A Markov-based multi-link tapped-delayline model for railway communications is established, where the Markov chains are introduced to model the birth and death state of multipath components in multi-link scenarios. Using the relationship between the correlation coefficients of complex random variables (RVs), the amplitude and the phase of taps with different delays are modeled as correlated RVs. The proposed channel model is implemented and validated with measurements.

Characterization and modeling of massive multiple-input multiple-output (MIMO) channel has been one of the research hotspots in the field of wireless communications. One important feature of the massive MIMO channel is the spatial non-stationarity. To statistically model the spatial non-stationary massive MIMO channels, a cluster-based channel model is proposed in this paper. The model incorporates both inter- and intra-cluster properties and the cluster evolution over the large-scale array. A hybrid data processing scheme is applied to extract the multipath components (MPCs) and clustering the MPCs over a large-scale antenna array. The global angular spread, cluster angular spread, and cluster delay spread are modeled with log-normal distributions. Observed cluster length and MPC length within clusters, which are introduced to describe the cluster existence over the array and MPCs existence within the cluster, respectively, are statistically modeled with the exponential distributions. Moreover, both the cluster and MPC arrival intervals, which are used, respectively, to describe the cluster occurrence position on the array and MPC occurrence position within the cluster, can be statistically modeled with the uniform distributions. Finally, the model implementation is validated by comparing with the different channel performance metrics between measurements and simulations.

With the rapid development of wireless communications, the understanding of three-dimensional (3D) propagation channels becomes essential for design and testing of some new wireless technologies, e.g., massive multiple-input multiple-output (MIMO) and full dimensional beamforming. To not only fully exploit the 3D multiplexing but also circumvent the size limitation of base station (BS), antenna elements in massive MIMO are usually arranged both horizontally and vertically. Based on an elaborate channel measurement campaign conducted at 11 GHz in a lobby environment, a 3D extended cluster-based channel model is proposed in this paper for massive multiple-input single-output (MISO) multi-user communications. In the model, the channel characteristics in both azimuth and elevation dimensions, and the visibility regions which are parametrized by observed cluster lengths across the large-scale array in both horizontal and vertical directions, are taken into consideration. Moreover, the spherical wavefront phenomenon observed from the measurements is also incorporated in the model. Model parametrization, implementation, and validation are presented in detail. Validations show that the proposed model can accurately reflect the realistic channel, and the spatial non-stationarity and the spherical wavefront should be carefully considered in the channel models for large-scale array communications.

With the advent of vehicle-to-vehicle (V2V) communication, the research on the propagation channel modeling between vehicles becomes a vital topic. In this paper, measurements of V2V radio channel are conducted in a suburban scenario. The measurements are performed at the center frequency of 5.9 GHz with a bandwidth of 50 MHz. A single omnidirectional antenna is placed at the transmitter (Tx) vehicle, and a 16 dual-polarized cylindrical antenna array is adopted at the receiver (Rx) vehicle. The multipath components (MPCs) are extracted based on the Space- Alternating Generalized Expectation-maximization (SAGE) algorithm, and we also apply an automatic clustering and tracking algorithm to cluster the MPCs and track the time-variant clusters for our measured V2V channel. Under such a scheme, a time- variant cluster-based channel modeling approach is proposed. The model parameters include a number of inter-cluster parameters and some intra-cluster parameters, which are provided with detailed analysis. The proposed model is useful for characterizing the time-variant V2V channel with high accuracy and low complexity.

Massive multiple-input multiple-output (MIMO) has been selected as one of the key technologies of the fifth generation mobile communication system (5G). It can provide high spectral and power efficiency, thus it is suitable to be deployed in different hotspot scenarios. In this paper, a channel measurement campaign using a wideband channel sounder and a 256-element virtual uniform rectangular array (URA) for massive MIMO communications is presented. The measurements were respectively conducted at 6 GHz and 11 GHz, and the subway station environment was considered. The typical channel parameters, root mean square (RMS) delay spread and coherence bandwidth, are analyzed based on the measurements. Moreover, the channel characteristics in angular domain are obtained by applying the space-alternating generalized expectation-maximization algorithm (SAGE). The SAGE estimates are validated by relating them to the physical environment. The overall elevation angle distribution of multipaths is found to be fitted well with Laplace distributions. The global angular spread, including the azimuth spread of departure (ASD) and elevation spread of departure (ESD), are both fitted well with Lognormal distributions. The results in this paper can be fed into the new channel simulator for massive MIMO, and are useful for the design and application of the practical massive MIMO system in the future.

Massive multiple-input multiple-output (MIMO) is one of the key technologies of the fifth generation mobile communication system (5G). For development of massive MIMO systems, the directional properties of their wireless channel are of great importance. In this paper, directional analysis is presented based on a channel measurement campaign for massive MIMO communications in a theater environment. The measurements were conducted at 11 GHz with a bandwidth of 200 MHz. A 256-element virtual uniform rectangular array (URA) was used during the measurements, and the channel characteristics in angular domain are determined by applying the space-alternating generalized expectation-maximization algorithm (SAGE). According to the SAGE estimates, channel nonstationarity over the large-scale array is discussed. The dominant scatterers are identified, by directly relating the SAGE estimates to the physical objects in the measurement environment. Moreover, direction spread over the large-scale array at the Tx side is estimated, and channel performance on beamforming is evaluated. Corresponding analysis is given in detail. These results are useful for the design and application of the practical massive MIMO system in the future.

In this paper, we present a measurement campaign of indoor massive MIMO channels by using a wideband channel sounder and a 64-element virtual linear array. The measurements are conducted at 6 GHz, with a bandwidth of 200 MHz. Both the light-of-sight (LOS) and obstructed-line-of-sight (OLOS) scenarios are considered in measurements. The Frequency Domain Space-Alternating Generalized Expectation maximization algorithm (FD-SAGE) is used to determine the channel characteristics in angular domain. In order to validate the obtained FD-SAGE estimates, the power azimuth spectrum (PAS) has been calculated using the Bartlett Beamformer (BBF). The non-stationarity of radio channels, which is reflected by power of the estimated MPCs, azimuth of departure (AOD), and azimuth of arrival (AOA), is discussed. The direction spread is estimated, which reflects spatial dispersion of wireless channel. It is also found that the diffuse scattering components at 6 GHz in OLOS scenario is richer than that in LOS scenario, and the magnitude of the diffuse scattering components are comparable with the specular components. It is suggested that the non-stationarity of the diffuse scattering components should not be neglected in the indoor massive MIMO channel modeling at the frequency bands below 6 GHz.

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss, shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures, such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels, which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.