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.

In this paper, a 3D geometrical cluster-based channel model is developed for reconfigurable intelligent surface (RIS)-assisted multi-user communication systems. Herein, spatial consistency of multi-user channels is considered to guarantee smooth evolution of large-scale (i.e. Ricean K-factor, shadow fading, and number of clusters) and small-scale (i.e. distance and angle of multipath components) channel parameters for base station (BS)-receiver (Rx) and BS-RIS-Rx links of different users. Two different optimization objectives for multi-user channels are presented, namely, maximizing sum of channel gains (O1) and maximizing channel capacity (O2). Then, projected gradient ascent algorithm is applied to solve RIS phase shift matrix under both optimization objectives. Furthermore, some channel statistical characteristics, namely, correlation coefficient, singular value spread, channel capacity, and root mean square (RMS) delay spread are derived and analyzed. At the same time, impacts of solved RIS phase matrices under different optimization objectives, as well as locations of RIS and users, on channel characteristics are compared and investigated. Simulation results demonstrate that the role of RIS under O1 is to align path phases of BS-RIS-Rx link with those of BS-Rx link as much as possible, whereas under O2, the RIS aims to enhance power while reducing user channel correlation. In addition, solved RIS phase under O2 can increase channel capacity and reduce channel correlation between different BS antennas, whereas O1 leads to a reduction in channel capacity. Location of RIS is also found to affect channel response impulse amplitude of different users. Furthermore, accuracy of the proposed model is verified by comparing RMS delay spread with measured data. These observations will provide a foundation for developing reconstructed wireless propagation environment of future multi-user communication systems.

In this paper, a novel vision transformer (ViT) network is proposed to adaptively construct radio multi-user channels via reconfigurable intelligent surface (RIS), with objective of enhancing received power at the required users’ locations. Different from deep learning scheme that solves RIS phase by learning relationship between the multi-user channel matrix and the RIS phase, channel impulse response (CIR) image is used as input for ViT network, which unifies representation of channels with arbitrary numbers and positions of users. Thus, the network can adaptively solve RIS phase for arbitrary channel scenarios with random user positions and different numbers of users, realizing to reconstruct channels with the required characteristics. Technically, in train stage, local CIR image sets are used as input and RIS phase sets are used as output to train ViT network. Herein, RIS phase sets are determined by closed-form solution and deep neural network for single- and multi-user scenarios, respectively. The CIR image sets are generated by a geometry-based channel model and solved RIS phase. In prediction stage, the desired CIR images, generated based on users’ locations and a mask model, serve as inputs for the trained ViT network to predict RIS phase. Functional verification of ViT network is conducted by comparing differences between CIR image generated by predicted RIS phase and original CIR images input into network. At the same time, different optimization schemes, including convolutional neural network, deep neural network, projected gradient ascent algorithm, and coordinate descent algorithm are compared with ViT network to evaluate prediction performance. The results show that the solved RIS phase by ViT network can achieve the largest coverage probability and magnitude of CIR among the other compared algorithms. At the same time, generalization capability of the above optimization schemes is evaluated under different initial user numbers. The Furthermore, impacts of some parameters related to the desired CIR image generation, such as noise range and bound, on ViT network performance are investigated. These observations can present a reference for intelligent design of communication environment via RIS.

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.

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.

In diverse application scenarios of wireless communications, such as massive MIMO channel estimation and sparse multi-user detection, recovering structured sparse signals from linear measurement models under non-ideal conditions remains two key challenges. First, existing sparse prior models are not flexible enough to adapt to the diverse structured sparse properties encountered in various application scenarios, including block sparsity, hierarchical sparsity, and group sparsity. Second, the parameter deviations in the measurement matrix and the potential correlation between its elements seriously impair the robustness of traditional compressed sensing (CS) algorithms. To address these challenges, this paper proposes a three-layer iterative optimization architecture based on joint angle-delay sparsity structure (JADSS). At the same time, the message passing and variational bayesian inference (VBI) methods are embedded in the JADSS iterative architecture, and the JADSS-VBI algorithm is proposed, which significantly improves the signal recovery accuracy under non-ideal conditions. The simulation results show that the JADSS-VBI algorithm exhibits excellent channel estimation performance.

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.

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of the fifth generation (5G) has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, the sixth generation (6G) is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an artificial intelligence (AI)-enabled cross-domain channel modeling and orthogonal time–frequency space–time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications (SACs), AI-assisted Internet of Things (IoT), semantic communications (SCs), and digital twin (DT) networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed. © 2026 IEEE

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.

Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support the ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typically employed in THz systems. To address these challenges, we propose a novel low-complexity two-phase channel estimation scheme for RIS-assisted THz systems with beam split effect. In the proposed scheme, we first estimate the full CSI over a small subset of subcarriers (SCs), then extract angular information at both the base station and RIS. Subsequently, we recover the full CSI across remaining SCs by determining the corresponding spatial directions and angle-excluded coefficients. Theoretical analysis and simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean-square error while significantly reducing computational complexity compared to existing algorithms.

Reconfigurable intelligent surface (RIS)-aided tera-hertz (THz) communication is considered as a promising technique for the sixth-generation network. However, employment of hybrid beamforming in THz systems introduces beam split effect, resulting in severe achievable data rate loss. To realize the full potential of RIS-aided THz systems, it becomes crucial to acquire accurate channel state information. Therefore, in this paper, we propose a novel cyclic beam split generalized approximating message passing (CBS-GAMP) channel estimation scheme without requiring the knowledge of number of propagation paths. Specifically, we expand cascaded channels into sparse representations by designing a CBS dictionary, and then we propose the CBS-GAMP algorithm based on statistical inference framework. Numerical simulations demonstrate effectiveness of the proposed CBS-GAMP scheme against the existing solutions.

In this paper, a 3D geometry-based stochastic channel model (GBSM) is proposed for RIS-assisted multi-user communications. The proposed GBSM is divided into two sub-channels, that is, BS-RIS and RIS-Rx links, and propagation distances and angles of multipath components are derived to describe multi-user channels. In addition, optimization objective for multi-user channel is proposed, and deep reinforcement learning is introduced to solve high-dimensional RIS phase problem. Based on the proposed model and solved RIS phase, channel capacity and root mean square delay spread are derived. The simulation results show that RIS optimization parameters and channel parameters have major impact on channel characteristics. The conclusions can provide a reference for designing and developing of RIS-assisted multi-user systems.

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.

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals encounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) cell-free massive multiple-input-multiple-output (mMIMO) networks have been envisioned as a prospective technology for future 6G networks. However, due to the ultrawide bandwidth and the frequency-dependent characteristics of RISs, beam-split effect has become an unavoidable obstacle. To compensate the severe performance degradation caused by beam split effect, we introduce additional time delay (TD) layers at both access points (APs) and RISs. Accordingly, we propose a joint precoding framework at APs and RISs to fully unleash the potential of the considered network. Specifically, we first formulate the joint precoding as a nonconvex optimization problem. Then, given the location of unchanged RISs, we adjust the TDs of APs to align the generated beams toward RISs. After that, with knowledge of the optimal TDs of APs, we decouple the optimization problem into three subproblems of optimizing the baseband beamformers, RISs and TDs of RISs, respectively. Exploiting multidimensional complex quadratic transform, we transform the subproblems into convex forms and solve them under alternate optimizing framework. Numerical results verify that the proposed method can effectively mitigate beam split effect and significantly improve the achievable rate compared with conventional cell-free mMIMO networks.

Deploying reconfigurable intelligent surfaces (RIS) near source and destination is of practical interest for improving the link quality of vehicle-to-vehicle (V2V) wireless systems. However, the accurate channel modeling and RIS tile deployment constitute a pair of challenges to evaluate the system performance such as error performance, capacity, etc. In this paper, we investigate the double-RIS channel characteristics and propose a geometry-based triple-cylinder model, where the RIS sub-surface/tile is enabled to assist V2V systems and other elements are turned off. To determine tile locations on RIS surface, we formulate an optimization problem by maximizing the end-to-end channel gain and solve it using gradient ascent (GA) method. Following this, channel correlation function, channel capacity, and outage probability are derived according to the proposed model. Five typical mobile scenarios are discussed to validate the convergence of proposed GA algorithm, where the results show that channel gain can converge to its maximum with optimized tile locations. In addition, channel correlation under different parameters and conditions are explored. Simulation results validate the enhanced channel capacity and outage probability obtained by optimizing the tile locations.

Utilizing unmanned aerial vehicles (UAVs) to provide reliable and effective wireless communication services for ground users has become a promising solution in emergency scenarios. However, energy consumption limitations lead to new challenges to timeliness of UAV communications. In this article, we propose a service time optimization strategy using UAV as an aerial base station. Total service time of UAV, including hover time and flight time, is minimized while satisfying user load demand. First, the K-means algorithm is used to complete area division. Under the constraints of UAV coverage and transmit power, hover positions of UAV in each partition are optimized to obtain the shortest hover time. Subsequently, the shortest flight time is obtained by solving the traveling salesman problem. Finally, the above process is integrated into a complete service time optimization problem. Simulation results show that the proposed strategy successfully achieves full-area coverage with the shortest service time, which significantly outperforms other existing algorithms, and there exists an optimal altitude to minimize the total service time.

In this paper, a 3D geometry-based reconfigurable intelligent surface (RIS)-aided channel model is proposed with directional antenna at base station (BS) side. Different from traditional RIS-assisted channel model, visible area (i.e., range of observed scatters) and radiated power of directional antenna with uniform, cos and Gaussian patterns are considered. The time-varying large- and small-scale parameters of complete communication links including BS-receiver, BS-RIS, and RIS-receiver links are modeled considering intensively line-of-sight (LoS) and non-LoS (NLoS) paths. Three transmission cases at BS antenna side, that is, LoS Case, Mixed Case, and RIS Case are defined by directivity and number of beams. The principle of case selection is to maximize absolute value of CIR as optimization objective. Based on the proposed channel model and directional transmission cases, some second-order statistical characteristics, such as space-time correlation function and Doppler power spectrum density are derived, and conversion between different cases causes the simulation results to vary discontinuously in time or space domains. Furthermore, impacts of different cases and switching of direction antenna, radiated patterns of directional beam, RIS optimization phase, and environment conditions on channel characteristics are investigated. The results show that the proposed case selection method can well enhance correlation and lead to correlation taking on a sawtooth shape. In addition, it is also able to reduce the Doppler power spectrum curve. Furthermore, the proposed model is validated by comparing root mean square delay spread with measurement. These observations can be used to present a reference for designing and evaluating directional RIS-assisted communication systems.

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.

To meet increasing applications of Internet of Things (IoT), the 3rd generation partnership project has specified Narrow Band Internet of Things (NB-IoT) standard. However, collisions in NB-IoT physical random access channel (NPRACH) can be severe due to mismatch between frequent random access requests of a huge number of devices and limited available preambles. In this paper, we propose to use non-orthogonal frequency hopping between preambles and introduce controllable partial collisions to effectively avoid full preamble collisions. This will increase available preambles and thus reduce preamble collision significantly. The proposed hopping pattern maintains compatibility with the current standard NB-IoT system, by keeping NPRACH structure in standard with minimal change. Simulation results show that the proposed frequency hopping pattern can greatly increase accessing devices due to collision avoidance while slightly reducing detection probability.

Intelligent reflecting surface (IRSs) have recently played a crucial role for numerous application associated with unmanned aerial vehicle (UAV) communications. To provide good quality of service (QoS) for the users, it is necessary to study the IRS-aided UAV communication system with performance analysis. Thus, in this paper, a three-dimensional (3D) geometry-based stochastic multiple-input multiple-output (MIMO) channel model is developed to characterize the IRS-aided air-to-ground (A2G) propagation environments, which takes into account the multipath fading and height-dependent path loss effects. Based on the proposed channel model, the impact of some important system parameters on the outage probability (OP) is numerically investigated. One key insight from our analysis is the AIRS operating on mmWave frequencies is more suitable for high mobility scenarios. The obtained results can provide guidance for achieving better system performance optimization.

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.

Reconfigurable intelligent surface (RIS) has received increasing attention due to its capability of extending cell coverage by reflecting signals toward receivers. This paper considers a RIS-assisted high-speed train (HST) communication system to improve coverage probability. We derive the closed-form expression of coverage probability. Moreover, we analyze impacts of some key system parameters, including transmission power, signal-to-noise ratio threshold, and horizontal distance between base station and RIS. Simulation results verify the efficiency of RIS-assisted HST communications in terms of coverage probability.

We propose a new wireless paradigm for railways – transmissive metasurface (TMS) assisted communications. It compensates for the Doppler shift brought by high mobility and reduces signal degradation due to train carriages. Specifically, the elements of the TMS panel attached to train windows manipulate the links between the base station (BS) and the onboard users. One fundamental problem with it is: does the channel introduced by TMS outperform the traditional direct channel? To answer this, we compare the gains of direct and cascaded channels and introduce the cascaded-outperform-direct probability (CODP), which is the probability that the latter exceeds the former. We then derive two simplified closed-form CODP expressions by approximating the cascaded channel gain as mixed Gaussian and Gamma distributions. Moreover, BS-related parameters impact the CODP; we show that the CODP has (i) global maximum points concerning the azimuth angle of the path from the BS to the TMS, the distance from the BS to the railway, and the BS height, respectively, and (ii) a minimum point in terms of the azimuth angle of the path from the BS to the TMS. Finally, we provide numerical results to verify our analysis and derivations.

The emerging intelligent transparent surface (ITS), unlike the intelligent reflection surface (IRS), allows incident signals to penetrate it instead of being reflected, which enables the ITS to combat the severe signal penetration loss for high-speed railway (HSR) wireless communications. This paper thus investigates the channel estimation problem where the ITS is attached to the HSR carriage window. We first propose a new transmission scheme with two pilot blocks for each frame. Second, we formulate the channels as functions of physical parameters and thus transform the problem into a parameter recovery problem. Third, we develop a successive closed-form, maximum likelihood (ML) channel estimation algorithm. Specifically, each estimate is expressed as the sum of its perfectly known value and the estimation error. By leveraging the relationship between channels for the two pilot blocks, we eliminate the unknown parameters besides Doppler shifts, which can be thereby recovered. With the reconstructed Doppler-induced phase shifts, we acquire other channel parameters. Moreover, the Cramér-Rao lower bound (CRLB) for each parameter is derived as a performance benchmark. Finally, we provide numerical results to establish the effectiveness of our proposed estimators.

Recently, unmanned aerial vehicle (UAV)-assisted ground targets localization is widely used in search and rescue (SAR) scenes. In this article, we propose a UAV search and localization strategy, which can search and locate unknown number of victims. The proposed strategy uses the UAV as a mobile anchor to measure time of arrival (TOA) and can effectively mitigate localization errors caused by non-line-of-sight (NLOS) propagation. Generally speaking, the strategy is divided into two parts: 1) trajectory planning and 2) localization. By selecting waypoints and planning a suitable search trajectory, UAV can obtain all the measurement information in deployment area and ensure that each target has at least three anchors for localization even in the NLOS propagation environment. In the localization stage, a new estimator is proposed based on maximum-likelihood estimation (MLE), which estimates average NLOS bias together with the coordinates of target and can be well solved by the particle swarm algorithm. The simulation results show that the proposed strategy outperforms for mitigating NLOS error compared with other methods, and can effectively ensure high localization accuracy in the NLOS propagation environment.

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.

Piezo-Acoustic Backscatter (PAB), one type of near-zero power backscatter, is a new technology that realizes underwater communications. In this paper, we first describe the PAB node and the channel. Then we set up a signal transmission model and formulate the underwater channel estimation problem. Based on the sparse characteristics of the channel and compressed sensing theory, we use the gradient projection for sparse reconstruction (GPSR) algorithm to solve the underwater channel estimation problem and obtain the channel estimate. Simulation results prove that in the underwater PAB system, the GPSR algorithm offers better estimation accuracy than the traditional LS algorithm.

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. In this paper, a geometric RIS-assisted multiple-input multiple-output channel model is proposed for fixed-to-mobile (F2M) communications based on a three-dimensional cylinder model. The receiver is regarded as in motion, and the time-varying angles and propagation distances are derived to describe non-stationary channels. Based on the proposed channel model, the effect of RIS on spacetime correlation function is investigated. The results can be used to evaluate and optimize the performance of RIS-assisted F2M communication systems.

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.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

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.

In the real propagation environments, multipath components (MPCs) in wireless channel usually exist as clusters. Cluster based structure of MPCs has been widely used in wireless channel modeling. In this paper, a novel MPC clustering algorithm is proposed based on spectral clustering. Considering that MPCs having strong power should usually be grouped into different clusters, the algorithm introduces a power-weighted processing to identify the similarity of each MPC. The process of weighting the power is conducted by generating similarity matrix using the full link method of Gaussian kernel function in the traditional spectral clustering algorithm. In order to achieve high clustering accuracy, the dimensionality reduction method of Laplacian Eigenmap is decomposed by using the normalized cut method, the obtained eigenvectors are re-clustered to cut the generated similarity matrix for MPC clustering. In the simulation results, it is found that this algorithm can well separate MPCs with high powers into different clusters and achieves better clustering performance compared with KPowerMeans, Kmeans, and the traditional spectral clustering algorithms.

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.

The fifth generation (5G) wireless communication networks are expected to support communications with high mobility, e.g., with a speed up to 500 km/h. Hence 5G communications will have numerous applications in high mobility scenarios, such as high speed railways (HSRs), vehicular ad hoc networks, and unmanned aerial vehicles (UAVs) communications [1]-[3]. The 5G systems will provide advanced communication platforms enabling reliable transmission for the Wireless Train Backbone (WLTB) or Wireless Train Control & Management System (WTCMS) [4], [5]. They will also enable new services or enhancements for vehicular communications in Intelligent Transportation System (ITS) [6]-[9]. The coordination and swarming control for UAVs will also benefit from 5G capabilities, as UAV-based 5G infrastructure modeling and improvement have begun receiving attention [10]. In general, high mobility communication is not only about how large is the maximum speed, it is more about the challenges caused by mobility. In high mobility scenarios, a wireless channel is rapidly time varying, Doppler shifts and spreads can be much larger than those in cellular communications, and if modeled statistically, the channel will be non-wide-sense stationary (non-WSS) over a short time period. In addition, network topology can change quickly, and switching among base stations (BSs) and/or peer nodes can be more frequent, not forgetting 5G challenges in cross-border mobility [11].

Ambient backscatter devices (tags and readers) use existing radio frequency (RF) signals to transmit data. Most prior works consider single-antenna tags, but this paper investigates the case of multiple-antenna tags, which are capable of simultaneous energy harvesting and data transmission. However, the multi-antenna channel between the tag and the reader, and the unpredictable nature of RF signals due to uncontrollable RF sources (e.g., location and transmit power), make signal detection highly challenging. Thus, the detection process becomes a hypothesis testing problem with unknown parameters. Consequently, we design a blind detector based on the generalized likelihood ratio test (GLRT) without using channel state information (CSI), signal power and noise variance. The decision threshold and detection probability of it are also analyzed in detail. Furthermore, to maximize its detection performance, we develop the optimal backscatter antenna selection scheme. Interestingly, we show that the detector performs best when only two backscatter antennas are selected. Finally, extensive simulation results validate the analysis and illustrate the effectiveness of the proposed detector.

Discriminating between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, orLOS identification, is important for a variety of purposes in wireless systems, including localization and channel modeling. LOS identification is especially challenging in vehicle-to-vehicle (V2V) networks since a variety of physical effects that occur at different spatial/temporal scales can affect the presence of LOS. This paper investigates machine learning techniques for LOS identification in V2V networks using an extensive set of measurement data and then develops robust and efficient identification solutions. Our approach exploits several static and time-varying features of the channel impulse response (CIR), which are shown to be effective. Specifically, we develop a fast identification solution that can be trained by using the power angular spectrum. Moreover, based on the measurement data, we also compare three different machine learning methods, i.e., support vector machine, random forest, and artificial neural network, in terms of their ability to train and generate the classifier. The results of our experiments conducted under various V2V environments, which were then validated using$K$-fold cross-validation, show that our techniques can distinguish the LOS/NLOS conditions with an error rate as low as 1%. In addition, we investigate the impact of different training and validating strategies on the identification accuracy.

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.

Unmanned aerial vehicles (UAVs) communications are considered as a promising technology in various areas. In this paper, a three-dimensional (3D) non- stationary geometry-based stochastic model (GBSM) is proposed for UAV air-to- air (A2A) communication environments. The proposed GBSM considers not only both the ground surface and roadside reflections, but also the arbitrary trajectories of both UAV terminals. Based on the proposed model, some important statistical properties such as time- variant time-frequency correlation function and the Doppler power spectrum are derived and analyzed. Finally, numerical results show that a variation of the velocity and moving direction of the UAV has major impacts on the statistical properties of the radio channels, which indicates its usefulness for the performance analysis of UAV communication systems under non- stationary conditions.

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.

Recently, ambient backscatter has been introduced as an attractive technology that allows small devices, such as battery-less sensors and passive tags, to communicate by using radio-frequency (RF) signals over the air. It is worth noting that multiple-antenna tags could perform energy harvesting and backscattering simultaneously and thus are advantageous for ambient backscatter communication systems. Therefore, in this paper, we consider the ambient backscatter communication systems with multiple-antenna tags and focus on signal detection problem. Multiple antennas imply multiple channel parameters, which are difficult to estimate because the tags have limited power and can transmit few training symbols. To address this problem, a semi-blind detector is designed for readers to recover tag signals without any knowledge of the multiple channel parameters between the reader and the tag. We also derive the bounds on the detection probabilities. Moreover, an antenna selection scheme is suggested to optimize the detection performance. Finally, simulation results are provided to corroborate our theoretical studies.

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.

Non-stationary mobile-to-mobile (M2M) channel modeling has gained strong momentum as it is vital for developing M2M communications technology. Traditional geometry-based channel models (GSCMs) for M2M communications usually assume fixed velocity and moving direction, which differs from the realistic M2M scenarios and also makes it difficult to incorporate non-stationarity of channel into the regular-shaped GSCMs. In this paper, a mobility model-based method is proposed to incorporate non-stationarity into M2M channel modeling by introducing dynamic velocities and trajectories. A revised Gauss-Markov mobility model is first presented together with the cluster-based two-ring M2M reference model. The mobility model uses tuning parameters to adjust the degree of mobility randomness and covers different M2M mobility trajectories. Then, a closed-form time-variant time-frequency correlation function and the Doppler power spectrum are derived from the model. Based on the numerical analysis, it is found that for a regular-shaped GSCM with a fixed M2M scattering environment, the motion does not introduce non-stationarity, however, the dynamic motion (i.e., the changes of velocity and moving direction) leads to non-stationarity, which is reflected by the time-variant time correlation function and Doppler spectrum. Different propagation modes, cluster number, and intra-cluster nonisotropic scattering also have major impacts on channel non-stationarity. Moreover, the randomness of the mobility model is found to significantly increase the degree of channel non-stationarity. These conclusions are useful for M2M non-stationary channel simulation and communication system evaluation.

Channel measurements of large-scale multiuser multiple-input multiple-output (MU-MIMO) radio propagation channels are presented. In the setup, three users with patch antennas communicate simultaneously with a base station (BS) equipped with a large antenna array in an indoor environment. Both a uniform linear array (ULA) and a uniform planar array (UPA) are used, and their relative ability to separate MU-MIMO signals is examined. At the mobile station (MS) side, the effect of inter-user spacing (i.e., the spacing between different users) is investigated. This evaluation is done by means of the correlation matrix distance metric (between each pair of users) and the singular value spread of the system. Our investigation shows that the users can be spatially separated in a large antenna array system in line-of-sight propagation conditions even when they are located close to each other. Furthermore, the users tend to be more separable when a ULA is adopted, compared to using a UPA. Finally, we also confirm that larger user separation distance results in increased channel orthogonality by measurements.

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.

In the real-world environments, multipath components (MPCs) of wireless channels are generally distributed as groups, i.e., clusters. Modeling the clustered MPCs is important and necessary for channel modeling and an automatic clustering algorithm is thus required. This paper proposes a novel Kernel-power-density (KPD) based algorithm for MPC clustering. It uses the Kernel density to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. The proposed algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. Simulations validate the KPD algorithm and almost no performance degradation is found even with a large number of clusters and large cluster angular spread. The KPD algorithm enables applications with no prior knowledge about the clusters such as number and initial locations. It can be used for the cluster based channel modeling for 4G#x002F;5G communications.

The millimeter wave (mmWave) communications and massive multiple-input multiple-output (MIMO) are both widely considered to be the candidate technologies for the fifth generation mobile communication system. It is thus a good idea to combine these two technologies to achieve a better performance for large capacity and high data-rate transmission. However, one of the fundamental challenges is the characterization of mmWave massive MIMO channel. Most of the previous investigations in mmWave channel only focus on single-input single-output links or MIMO links, whereas the research of massive MIMO channels mainly focus on a frequency band below 6 GHz. This paper investigates the channel behaviors of massive MIMO at a mmWave frequency band around 26 GHz. An indoor mmWave massive MIMO channel measurement campaign with 64 and 128 array elements is conducted, based on which, path loss, shadow fading, root-mean-square (RMS) delay spread, and coherence bandwidth are extracted. Then, by using our developed ray-tracing simulator calibrated by the measurement data, we make the extensive ray-tracing simulations with 1024 antenna elements in the same indoor scenario, and get insights into the variation tendency of mean delay and the RMS delay with different array elements. It is observed that the measurement and the ray-tracing-based simulation results have reached a good agreement.

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.

Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multipleinput-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for clusterbased channel modeling.

Due to the extra loss resulting from tunnel curve, most of the theoretical models cannot be directly used for propagation inside curved tunnels. In this paper,extensive simulations are made for propagation at different frequencies in different types of curved tunnels, by using a ray-tracing simulator that is validated by numerous measurements. From the simulated received power, the extra losses of tunnel curve in various cases are extracted, analyzed, and stochastically modeled by normal distributions. Thus, with the parameters presented in this paper, the total propagation loss inside curved tunnels can be predicted by adding the stochastically generated extra loss to the propagation loss in the straight tunnel. This provides a fairly simple and effective way to extend the theoretical propagation models to fulfill network planning and system design for communication systems in real curved tunnels.

In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which exploits the feature of CIR that power of multipath component (MPC) is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted ℓ1minimization. Then a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. The proposed algorithm incorporates the physical behaviors of MPCs into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. The results in this paper can be used to parameterize the CIR model of radio channels.

It has been widely recognized that relaying is an important method for increasing the reliability and spectral efficiency of communications systems, and it is thus helpful for improving the performance of vehicle-to-vehicle (V2V) communication systems. However, designing and evaluating V2V relay networks require understanding the effect of shadowing, as this critically impacts the performance of the relay system. Even though the theoretic performances of various relaying schemes have been well investigated, there is a lack of empirical test that incorporates realistic shadowing effects. In this paper, we analyze the performance of relaying transmission in V2V scenarios based on measurements in scenarios where shadowing occurs through large vehicles such as buses. We investigate several potential locations for the relay nodes, and the measurements are performed with two static transmitters (TX) and one dynamic receiver (RX). Outage probabilities of several relaying schemes such as multi-hop decode-and- forward, multi-hop amplify-and-forward, and diversity-amplify-and-forward are estimated and discussed based on the measured instantaneous end- to-end signal-to-noise ratio (SNR). It is found that: (i) shadowing effect caused by the bus between V2V line-of-sight (LOS) links increases the outage probability for the non-LOS (NLOS) direct transmission; (ii) using relay node on the bus roof can significantly improve transmission, however, a strong shadowing effect may reduces the acceptable communication distance of relaying scheme; and (iii) the diversity-amplify-and- forward relaying scheme generally has the best performance. Our results can be used to design a relay system for V2V communications.

Mobile Hotspot Network (MHN) is designed as a new communication system for high speed mobile group vehicles and aiming at providing multi-Giga bps data rate by using millimeter wave (mmWave). According to the MHN setup, the channel at 30 GHz band is simulated by a ray-tracing tool in a typical straight subway tunnel. In order to assess the system physical layer performance, system simulations based on entire channel characteristics provide an profound significance. the channel parameters such as path loss, power delay profile, decorrelation distance, Rician K-factor, Doppler characteristics, etc., are extracted and analyzed for verifying system feasibility and can serve as a guide for physical layer design.

Massive MIMO has been chosen as one of the candidate technologies of the fifth-generation mobile communication system (5G), and channel modeling of massive MIMO is of great importance. The most direct and effective approach to investigate the propagation characteristics of massive MIMO channels is channel measurements. However, there are only few measurements of massive MIMO channels, and there still lacks deep investigations of massive MIMO channel characteristics. In this paper, we present a measurement campaign of indoor massive MIMO channels, by using a linear large-scale array with 64 elements. The measurements are conducted at 2 GHz, 4 GHz, and 6 GHz, respectively, with a bandwidth of 200 MHz. Both LOS and NLOS propagation scenarios are considered in the measurements. The basic channel parameters are extracted, including path loss, delay spread, and coherence bandwidth. The non-stationarity of radio channels, which is reflected by the variations of delay spread and coherence bandwidth over different array locations, is discussed. The impact of carrier frequency on the above channel parameters is further discussed. The results would be useful for the design of massive MIMO system in the indoor environments.

The local average power estimation is needed by communications system for use in coverage assessment, power control, and handoff. Aiming at satisfying the broadband communications and higher safety requirements of next generation communications system, this paper proposes novel criteria of local mean power estimation, including the statistical averaging length, sample number, and sample interval. The multi-path fading is Nakagami-m distributed and the basic procedure is similar to Lee Criteria. The performance of the estimation algorithm is compared with the Lee method which is based on Rayleigh distribution. When it is LOS propagation, which covers the most cases in railway scenario, the measured length necessary to obtain the local average power is determined to be in the range of 10 to 25 wavelengths. The sufficient number of samples depends on one parameter of Nakagami-m distribution and varies from 1 to 14. It is based on the 95 percent and 99 percent confidence interval and less than 1 dB error in estimating. The sample interval increases greatly in comparison with Lee criteria, which can reduce the measurement overhead while remaining the high estimating accuracy.

The rapidly time-varying channel in high-speed railway poses tough design challenges, which necessitates the research of accurate channel models. Existing researches focus on the isolated high-speed railway scenarios, and mainly deal with path loss, shadowing fading, Ricean K-factor and delay spread. However, few studies have been done in Doppler domain. In this paper, a deterministic channel model that employs ray- tracing algorithm is presented. The proposed deterministic modeling approach is applied in composite high-speed railway scenario rather than isolated one. The scenario is flexibly reconstructed through SketchUp. The simulation results are validated by the data measured in the same scenario. The channel characteristics in Doppler domain and the effect of Doppler shift are statistically analyzed based on the deterministic channel model. The transition regions in the composite scenario are emphatically investigated, and the results are compared with those of prior studies.

This paper presents a detailed description of a propagation channel measurement campaign performed in a warehouse environment and provide a comprehensive channel model for this environment. Using a vector network analyzer, we explored both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios over a 2-8 GHz frequency range. We extracted both small-scale and large-scale channel parameters such as distance-dependent path loss exponent, frequency-dependent path loss exponent, shadowing variance, and amplitude fading statistics of the channel. We also provide the clustering analysis of the channel impulse responses by using a modified Saleh-Valenzuela approach. Our model is validated by comparing the distributions of the root-mean-square (RMS) delay spread obtained from our model and measurement data, respectively. The model developed can be used for realistic performance evaluations of ultrawideband (UWB) communications and localization systems in warehouse environments.

To statistically model time-variant vehicle-to-vehicle (V2V) channels, the dynamic multi-path components (MPCs) are characterized based on suburban measurements conducted at 5.3 GHz. The correlation matrix distance (CMD) is used to determine the size of local wide-sense stationary (WSS) region. Within each WSS time window, MPCs are extracted using wideband spatial spectrum of Bartlett beamformer. A MPC distance (MCD)-based tracking algorithm is used to identify the "birth" and "death" of MPCs over different WSS regions, and the lifetime of MPC is modeled with a truncated Gaussian distribution. Distributions of number of MPCs and their positions are statistically modeled. The MPC characterization considers both angular and delay domain properties as well as the dynamic evolution of MPCs over different WSS regions. The results shows insight into the dynamic behaviors of MPCs in V2V environments, and is useful for the scatterer modeling in the geometry-based stochastic channel modeling.

Wave propagation characteristics in curved tunnels are of importance for designing reliable communications in subway systems. This paper presents the extensive propagation measurements conducted in two typical types of subway tunnels—traditional arched “Type I” tunnel and modern arched “Type II” tunnel—with 300- and 500-m radii of curvature with different configurations—horizontal and vertical polarizations at 920, 2400, and 5705 MHz, respectively. Based on the measurements, statistical metrics of propagation loss and shadow fading (path-loss exponent, shadow fading distribution, autocorrelation, and crosscorrelation) in all the measurement cases are extracted. Then, the large-scale fading characteristics in the curved subway tunnels are compared with the cases of road and railway tunnels, the other main rail traffic scenarios, and some “typical” scenarios to give a comprehensive insight into the propagation in various scenarios where the intelligent transportation systems are deployed. Moreover, for each of the large-scale fading parameters, extensive analysis and discussions are made to reflect the physical laws behind the observations. The quantitative results and findings are useful to realize intelligent transportation systems in the subway system.

The high-speed railway (HSR) propagation channel has a significant impact on the design and performance analysis of wireless railway control systems. This paper derives a stochastic model for the HSR wireless channel at 930 MHz. The model is based on a large number of measurements in 100 cells using a practically deployed and operative communication system. We use the Akaike information criterion to select the distribution of the parameter distributions, including the variations from cell to cell. The model incorporates the impact of directional base station (BS) antennas, includes several previously investigated HSR deployment scenarios as special cases, and is parameterized for practical HSR cell sizes, which can be several kilometers. The proposed model provides a consistent prediction of the propagation in HSR environments and allows a straightforward and time-saving implementation for simulation.

High-speed railways (HSRs) have been widely introduced to meet the increasing demand for passenger rail travel. While it provides more and more conveniences to people, the huge cost of the HSR has laid big burden on the government finance. Reducing the cost of HSR has been necessary and urgent. Optimizing arrangement of base stations (BS) by improving prediction of the communication link is one of the most effective methods, which could reduce the number of BSs to a reasonable number. However, it requires a carefully developed propagation model, which has been largely neglected before in the research on the HSR. In this paper, we propose a standardized path loss/shadow fading model for HSR channels based on an extensive measurement campaign in 4594 HSR cells. The measurements are conducted using a practically deployed and operative GSM-Railway (GSM-R) system to reflect the real conditions of the HSR channels. The proposed model is validated by the measurements conducted in a different operative HSR line. Finally, a heuristic method to design the BS separation distance is proposed, and it is found that using an improved propagation model can theoretically save around 2/5 cost of the BSs.

Vehicle-to-Vehicle (V2V) communication is an en-abler for improved traffic safety and congestion control. As for any wireless system the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel in which a bus acts as a shadowing object between two passenger cars. The measurement setup is based on a WARP FPGA software radio as transmitter, and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the large-scale path loss, shadowing, and small-scale fading. The main effect of the bus is that it is acting as an obstruction creating an additional 15–20 dB attenuation. A Nakagami distribution is found to describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance-dependency of the path loss is analyzed, and a stochastic model is developed to reflect the impact.

Wireless sensor networks (WSNs) are formed by a large number of arbitrarily or randomly deployed sensing nodes, monitoring and measuring physical parameters extracted from the environment. The correlation of shadow fading caused by the similar propagation environments in wireless channel poses significant research challenges in system design. This paper proposes a general shadowing correlation model as a four-slope piecewise function of distance as well as angle according to different network topological structure and geographical deployment. It can be applied for the generation of channel model contains any pair of correlated communication links in WSNs environment.

High-speed railway (HSR) has been widely introduced to meet the increasing demand for passenger rail travel. While it provides more and more conveniences to the people, the huge cost of the HSR has laid big burden on the government finance. Reducing the cost of HSR has been necessary and urgent. Optimizing the arrangement of the base stations (BS) by improving the prediction of the communication link is one of the most effective methods, which could reduce the number of the BSs to a reasonable number. However, it requires a carefully developed propagation model, which has been largely neglected before in the research on the HSR. In this paper, we propose a standardized path loss model for HSR channels based on an extensive measurement campaign in 4594 HSR cells of the "Zhengzhou-Xian" HSR line. The measurements are conducted using a practically deployed and operative GSM-Railway (GSM-R) system to reflect the real conditions of the HSR channels. The proposed model is validated by the measurements conducted in another operative railway - "Beijing-Shanghai" HSR line. The results are helpful for the HSR communications system designers to gain a better tool in the system planning, and propagation researchers to assess where the most pressing needs in the modeling of HSR channels lie.

With the fast development of high-speed railways, the need for wireless communication to satisfy secure demand of train and application demand of passengers has become evident.As a part of large- scale propagation effects, shadow fading is of great importance to design hand-over algorithm, and to evaluate macro diversity gain in wireless communications. This paper offers an investigation on the cross-correlation property of shadow fading in high-speed railway (HSR) network, based on the empirical data measured along Xingyang to Luoyang HSR line of China. The measurements indicate that the crosscorrelation coefficient of shadow fading is correlated to the separation distance between two base stations. Several models, to describe the relation between the cross-correlation coefficient and the separation distance, are proposed and compared with each other in this paper, and a decaying exponential model is finally suggested to characterize the cross-correlation property in the HSR environments.

High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the main differences in scientific research for wireless communications between the HSR operation scenarios and the conventional public land mobile scenarios. The latest research progress in wireless channel modeling in viaducts, cuttings, and tunnels scenarios are discussed. The characteristics of nonstationary channel and the line-of-sight (LOS) sparse and LOS multiple-input-multiple-output channels, which are the typical channels in HSR scenarios, are analyzed. Some novel concepts such as composite transportation and key challenging techniques such as train-to-train communication, vacuum maglev train techniques, the security for HSR, and the fifth-generation wireless communications related techniques for future HSR development for safer, more comfortable, and more secure HSR operation are also discussed.

Vehicle-to-vehicle (V2V) communication is an enabler for improved traffic safety and congestion control. As for any wireless system, the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel, in which a bus acts either as a shadowing object or as a relay between two passenger cars. The measurement setup is based on a Wireless Open-Access Research Platform (WARP) Field-Programmable Gate Array (FPGA) software radio as transmitter and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the path loss, shadowing, small-scale fading, delay spread, and cross correlation. The main effect of the bus is that it is acting as an obstruction creating an additional 15- to 20-dB attenuation and an increase in the root-mean-square delay spread by roughly 100 ns. A Nakagami distribution is found to well describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance dependence of the path loss is analyzed and a stochastic model is developed.

The high-speed railway propagation channel has significant effect on the design and performance analysis of wireless railway control systems. An important feature of the high-speed railway communications is the usage of directional transmitting antennas, due to which the receiver may experience strong attenuation of the line-of-sight (LOS) path under the base station (BS). This leads to a drop, and strong variations, of the signal strength under the BS. While the physical origin of the signal variations is different from conventional shadowing, it can be described by similar statistical methods. However, the effect has been largely neglected in the literature. In this paper we first define the region of the bottom of the BS, and then present a simple shadow fading model based on the measurements performed in high-speed railways at 930 MHz. It is found that the bottom area of the BS has a range of 400 m – 800 m; the standard deviation of the shadowing also follows a Gaussian distribution; the double exponential model fits the autocovariance of the shadow fading very well. We find that the directivity of the transmitting antenna leads to a higher standard deviation of shadowing and a smaller decorrelation distance under the BS compared to the region away from the BS.

This paper reports (i) a set of measurements of the wireless propagation channel at 930 MHz, conducted along the "Zhengzhou-Xian" high-speed railway of China in various railway viaduct scenarios, and (ii) an analysis and modeling of the small-scale and large-scale channel parameters based on those measurements. The environment can be categorized into four cases, covering viaducts with different heights and in different suburban environments. Small values of fade depth, level crossing rates, and average fade duration are observed. Akaike's Information Criterion (AIC)-based evaluation indicates that the Ricean distribution is the best to describe small-scale amplitude fading. An analysis of the envelope autocovariance function shows that the coherence distance is less than 10 cm. The Ricean K-factor is modeled as a piecewise-linear function of distance. Moreover, a breakpoint path loss model is developed and shadow fading is investigated using the same break point as for the distance-dependent K-factor model. The Suzuki distribution is found to offer a good fit for the composite multipath/shadowing channels. We find that the viaduct height H, together with the number of surrounding scatterers, significantly affects the small- and large-scale channel parameters. These results are applicable to both normal-speed and high-speed railways, and will be useful in the modeling of railway viaduct channels and the design of railway wireless communication systems.

This paper presents analysis and spatial channel modeling based on a series of 930 MHz measurements conducted along the “Zhengzhou-Xi'an” high speed rail (HSR) of China. Raw data are collected in five cuttings in rural and suburban environments subject to the consideration of the geometry of cuttings, including crown width and bottom width. A comparison among different distributions of the small-scale-fading is made by using a root mean squared errors (RMSE) goodness-of-fit test, which shows that the Ricean distribution offers the best fit. Moreover, it is found that the rich reflection and scattering components in the cutting scenario reduce the effect of the dominant component of the received signal, which results in a Rayleigh distribution, validating a good fit in this line-of-sight (LOS) scenario. Due to the rich multipath components and the directional transmitting antennas in HSR, the Ricean K-factor is modeled as a piecewise function of distance associated with the median K-factor and a standard deviation. Our analysis shows that a “wide” cutting scenario, such as with shallower slopes and wide crown and bottom widths, can reduce the impact of channel fading.

This paper presents a set of 930 MHz measurements conducted along the “Zhengzhou-Xi'an” high-speed rail of China, to characterize short-term fading behavior of the rail viaduct scenario. Three measurement cases covering viaducts with different heights are reported. The analysis results include fade depth (FD), Ricean distribution fit and K-factor modeling, level crossing rates (LCR), and average fade duration (AFD). A small value of fade depth, around 15 dB, is observed. The Ricean distribution offers good fit in this line-of-sight (LOS) propagation scenario, and the K-factor estimated using moment-based method is modeled as a piecewise function, whose break point equals to the reference distance. It is found that the viaduct height H greatly affects the severity of fading and the feature parameters. The results are applicable to the design of high-speed rail communication systems and the modeling of the rail viaduct fading channels.

This paper proposes a distance-dependent Ricean K-factor model for a line-of-sight (LOS) viaduct scenario in the high-speed rail (HSR) of China. Extensive narrowband measurements conducted at 930 MHz are utilized. The propagation environment can be categorized into two cases: moderate suburban and dense suburban. The estimated K-factors are modeled as a piecewise-linear function of distance. The statistical fluctuations of K-factors are well considered by introducing the standard deviation to the expression. A detailed comparison between the piecewise-linear K-factor model and that of other literature validates the proposed model. Our results will be useful in the modeling of HSR viaduct channels and the performance analysis such as channel capacity and throughput for HSR wireless communication systems.

Communications Based Train Control Systems require high quality radio data communications for train signaling and control. Actually most of these systems use 2.4GHz band with proprietary radio transceivers and leaky feeder as distribution system. All them demand a high QoS radio network to improve the efficiency of railway networks. We present narrow band, broad band and data correlated measurements taken in Madrid underground with a transmission system at 2.4 GHz in a test network of 2 km length in subway tunnels. The architecture proposed has a strong overlap in between cells to improve reliability and QoS. The radio planning of the network is carefully described and modeled with narrow band and broadband measurements and statistics. The result is a network with 99.7% of packets transmitted correctly and average propagation delay of 20ms. These results fulfill the specifications QoS of CBTC systems.

The delay caused by the reflected ray in broadband communication has a great influence on the communications in subway tunnel. This paper presents measurements taken in subway tunnels at 2.4 GHz, with 5 MHz bandwidth. According to propagation characteristics of tunnel, the measurements were carried out with a frequency domain channel sounding technique, in three typical scenarios: line of sight (LOS), Non-line-of-sight (NLOS) and far line of sight (FLOS), which lead to different delay distributions. Firstly IFFT was chosen to get channel impulse response (CIR) h(t) from measured three-dimensional transfer functions. Power delay profile (PDP) was investigated to give an overview of broadband channel model. Thereafter, a long delay caused by the obturation of tunnel is observed and investigated in all the scenarios. The measurements show that the reflection can be greatly remained by the tunnel, which leads to long delay cluster where the reflection, but direct ray, makes the main contribution for radio wave propagation. Four important parameters: distribution of whole PDP power, first peak arriving time, reflection cluster duration and PDP power distribution of reflection cluster were studied to give a detailed description of long delay characteristic in tunnel. This can be used to ensure high capacity communication in tunnels.

This paper presents the results of path loss measurements in "Zhengzhou-Xi'an" high-speed railway environment at 930 MHz band. A transmitter directional antenna height of 20~30 meters above the rail surface and a receiver omni-directional antenna height of 3.5 meters were used on the high-speed viaducts height of 10~30 meters above the ground. An automatic acquisition system was utilized in the measurements. The model makes distinctions among different terrain. The results of measurements provide practical values for path loss exponent and standard deviation of shadowing affected by the viaduct factor in suburban, open area, mountain area and urban propagation regions where the high-speed trains travel. Based on the measurement data, the empirical path loss model was developed, which could be used for predicting the path loss for the future railway communication systems, and provide the facilities for network optimization.