The cell-free massive multi-input multi-output (CF-mMIMO) is a promising technology for the six generation (6G) communication systems. Channel prediction will play an important role in obtaining the accurate CSI to improve the performance of CF-mMIMO systems. This paper studies a deep learning (DL) based joint space-time-frequency domain channel prediction for CF-mMIMO. Firstly, the prediction problems are formulated, which can output the multi-step prediction results in parallel without error propagation. Then, a novel channel prediction model is proposed, which adds frequency convolution (FreqConv) and space convolution (SpaceConv) layers to Transformer-encoder. It is able to utilize the space-time-frequency correlations and extract the space correlation in the irregular AP deployment. Next, simulated datasets with different sizes of service areas, UE velocities and scenarios are generated, and correlation analysis and cross-validation are used to determine the optimal hyper-parameters. According to the optimized hyper-parameters, the prediction accuracy and computational complexity are evaluated based on simulated datasets. It is indicated that the prediction accuracy of the proposed model is higher than traditional model, and its computational complexity is lower than traditional Transformer model. After that, the impacts of space-time-frequency correlations on prediction accuracy are studied. Finally, realistic datasets in a high-speed train (HST) long-term evolution (LTE) network are collected to verify the prediction accuracy. The verification results demonstrate that it also achieves higher prediction accuracy compared with traditional models in the HST LTE network.

In recent years, satellite internet has been widely recognized as a key component of future integrated space-air-ground networks. With advancements in satellite miniaturization and launch technologies, mega-constellations have become a growing trend. The increasing number of satellites in constellations presents challenges for interference analysis. This paper proposes a novel interference analysis method based on time-elevation interference spectrum. The proposed method can provide a more comprehensive analysis for NGSO mega-constellations by considering the aggregated dynamic interference under the time-elevation domain. The interference characteristics under different orbital inclination, orbital plane numbers, orbital height, and ground station latitude are analyzed. Furthermore, the probability distributions of interference are derived based on the joint distribution of satellites and ground stations. The outage probabilities and throughput are also analyzed to measure the system’s availability. Through the validation of STK and the Monte Carlo method, our method has high accuracy.

Fifth generation new radio vehicle-to-everything (5G-V2X) communication is an emerging technology to support advanced use cases and higher automation levels in Internet of Vehicles. A comprehensive and accurate knowledge of narrow-beam channels plays a crucial role in the utilization of the 5G-V2X technique. This paper focuses on measurement and characterization of vehicle-to-infrastructure (V2I) narrow-beam channel. A flexible narrow-beam channel measurement system using a phased-array antenna is designed, and is employed to perform a series of 3.35 GHz channel measurements with different beam widths in highway primary road and auxiliary road. Based on the collected data, the fading characteristics of V2I narrow-beam channel including path loss, shadow fading and K-factor are extracted, analyzed and then modeled. Then, the V2I narrow-beam channel dispersion in time-frequency-space domain is characterized, and the statistical models of root-mean-square (RMS) delay spread, RMS Doppler spread and RMS angular spread are proposed. In addition, the V2I narrow-beam channel nonstationarity is discussed in terms of the stationarity interval and birth-death process of multipath components, and results of the Markov chain model with parameters like state transition probability matrix and steady-state probability are reported. The results can contribute to the design and evaluation of 5G-V2X technology.

Line-of-sight (LOS) or non-line-of-sight (NLOS) identification is of vital significance to the localization of mobile sensors in intelligent substations for power Internet of Things. This article investigates the LOS/NLOS identification in substation scenarios, based on deep learning networks and feature fusion methods. Channel measurement data in high-voltage substation environments with LOS and NLOS cases are collected, and both original and manually extracted channel features are obtained to generate data sets. A novel LOS/NLOS identification model is proposed, which employs a deep neural network and a self-attention network to separately learn the information contained in the manually extracted channel features and the original channel feature. This model also applies a hybrid fusion method to capture correlation between the channel features and mitigate data inundation risk caused by the dimension difference of input features. The results of performance evaluation show that the proposed model not only has the identification accuracy as high as 98.95%, but also possesses good noise robustness and acceptable computational complexity.

Vehicle-to-infrastructure (V2I) communications have attracted much attention in recent years due to its application in intelligent transportation systems. To apply the dynamic beamforming technology in V2I communications, an accurate V2I narrow-beam channel model is required. This paper investigates cluster-based dynamic narrow-beam channel modeling for V2I scenarios. Firstly, a phased-array antenna based channel measurement system is designed and used to perform a series of narrow-beam channel measurements at 3.35 GHz in V2I highway scenarios. Data processing methods such as beam synthesis, angle estimation based on single-scattering assumption, and variational Bayesian Gaussian mixture model clustering are applied to extract parameters of multipath components and clusters. Then, we propose a cluster-based dynamic narrow-beam channel model, which considers the directional attenuation of narrow-beam and non-stationarity of clusters. In this model, the parameters of spatial-temporal characteristics are divided into global-cluster parameters and scatterer-cluster parameters, and the statistical distribution of these parameters are studied and modeled. Finally, a simulation method for the proposed channel model is provided, and model validation results show a good match between measurements and simulations in terms of channel characteristics such as delay spread and angle spread. This model provides a better characterization of the V2I narrow-beam channel and will be useful for designing and evaluating V2I communication systems.

With the development of artificial intelligence (AI), wireless channel prediction based on deep learning (DL) has become a hot research issue. Channel prediction plays an important role in channel state information (CSI) feedback enhancement in AI-native air interface. To better predict the CSI, this paper investigates the Transformer network based channel prediction. Firstly, real channel data are obtained in Beijing-Tianjin railway line, and the channel prediction datasets are constructed through preprocessing. After formulating the channel prediction problem, a channel prediction model based on the Transformer network is newly proposed. The unique multi-head attention mechanism and position encoding of the Transformer network enable the proposed model to have more powerful parallel computation capability and better global information capture capability. Then, the hyper-parameters of the model are determined by autocorrelation analysis and cross-validation. Finally, the performance of the proposed model is evaluated in terms of prediction accuracy and space and time computational complexity using several evaluation metrics, and is compared with classical DL models. It is shown that the proposed model possesses higher prediction performance in the appropriate range of computational complexity.

An accurate and in-depth understanding of the narrow-beam channel is vital to support the application of 5G new radio-V2X (5G-V2X) communication in Internet of Vehicles. In this paper, we investigate the radio propagation measurements and characterization of the narrow-beam channel in typical vehicle-to-infrastructure (V2I) scenarios. The channel measurements based on a phased array antenna at 3.35 GHz have been performed in two typical V2I scenarios. The time-frequency-space dispersion of narrow-beam V2I channel has been characterized in terms of root-mean-square (RMS) delay spread, RMS Doppler spread and RMS angular spread, then the empirical models are established for the narrow-beam channel modeling. These results are helpful for the planning and optimization of 5G-V2X communication systems.

Wireless communication technologies play a key role to support the implementation of Power grid Internet of Things (PIoT). An in-depth knowledge of the radio channel is vital to the application of wireless communication technologies in PIoT. This article investigates the radio channel measurements and characterization for PIoT substation scenarios. A novel channel sounder is designed for achieving omnidirectional measurements and phased array antennas-based directional measurements, and a directional multipath components extraction algorithm is proposed. The channel sounding system is verified and is used to perform a series of 3.35-GHz omnidirectional and directional channel measurements in four substation scenarios, involving a 10-kV switch room, a 110-kV GIS room, a semi-indoor 110-kV substation, and an outdoor 220-kV substation. Based on the measured channel impulse response data, both large-scale and small-scale fading characteristics of substation channels are extracted and analyzed. Empirical models of path loss, shadow fading, Rician$K$-factor and root-mean-square (RMS) delay spread are proposed. In addition, results of RMS angle spread (AS) of departure and RMS AS of arrival are presented. These results will provide useful reference for the deployment and optimization of wireless communication networks in PIoT substation scenarios.

Accurate knowledge of non-stationary narrow-beam channel characteristics is the prerequisite of applying dynamic beamforming technology in high-mobility communication scenarios. This paper proposes a three-dimensional geometry-based stochastic model (GBSM) for non-stationary narrow-beam channels in high-mobility communication scenarios. In the proposed GBSM, the impact of antenna pattern on angular distribution and the birth-death process of clusters in space-time-frequency (STF) domain are considered. Statistical properties of the proposed model are derived, including STF correlation function and multi-link spatial cross-correlation function (CCF), and the corresponding numerical results are analyzed. Moreover, the proposed model is verified by channel measurements in two high-mobility communication scenarios, such as vehicle-to-infrastructure scenario and high-speed railway scenario, in terms of temporal autocorrelation function, frequency correlation function and multi-link spatial CCF. It shows a good agreement between the measurement and model results, which confirms the reliability of the proposed model.

An in-depth and accurate knowledge of the radio channel is vital to support the application of wireless communication networks in power Internet of Things (PIoT). In this paper, we investigate the radio propagation measurements and channel characterization for typical substation scenarios. The propagation measurements at 3.35 GHz have been performed in three high-voltage substation scenarios, involving a 10 kV switch room, a 110 kV gas insulated substation (GIS) room and an outdoor 220 kV substation. Based on the measured data, both large-scale and small-scale fading characteristics of substation channels are extracted and analyzed. Empirical models of path loss and shadow fading are proposed, and results of Ricean K-factor and root-mean-square (RMS) delay spread are presented. These results will provide a valuable reference for the deployment and evolution of wireless communication networks in high-voltage substation scenarios for PIoT.

Beamforming based on massive multiple-input multiple-output (mMIMO) is one of the promising technologies in future smart high-speed railways (HSR) communication systems. This paper proposes a non-stationary three-dimensional (3-D) wideband geometry-based stochastic model (GBSM) for HSR narrow-beam channels. The proposed GBSM employs a sphere model and two ellipsoid models to describe common and uncommon clusters for different links. The Gaussian beam pattern is considered to model the antenna characteristics at base stations (BSs) and array-time-frequency cluster evolution is used to describe the dynamics of scattering clusters. Moreover, statistical properties of the narrow-beam channel model such as the single-link space-time-frequency correlation function (STFCF) and multi-link spatial cross-correlation function (CCF) are investigated. Numerical results and analysis show that the proposed model is capable of characterizing the narrow-beam channels in the smart HSR communication systems.

Propagation scenario identification is of vital significance for boosting the performance of future smart high-speed railway (HSR) communication networks. This paper investigates the HSR propagation scenario identification model, based on deep learning networks and feature fusion methods. With the assist of railway long-term evolution (LTE) networks, the channel impulse responses are collected in four typical HSR scenarios including unobstructed viaduct, obstructed viaduct, station and suburban. Four channel characteristics involving power delay profile, root mean square (RMS) delay spread, RMS angular spread and Rice K-factor form the datasets used for model training and testing. Then, a novel propagation scenario identification model is proposed by merging a weighted score based feature fusion method into the long short-term memory (LSTM) neural network. The hyperparameters such as time window length and numbers of hidden units and layers are determined by autocorrelation analysis and cross-validation. Finally, the model performance is evaluated by focusing on the impact of different feature fusion methods and computational complexity.

Propagation scenario identification is of vital significance for boosting the performance of future smart high-speed railway (HSR) communication networks. This paper investigates the HSR propagation scenario identification model, based on deep learning networks and feature fusion methods. With the assistance of railway long-term evolution (LTE) networks, we collected the channel impulse responses in four typical HSR scenarios including unobstructed viaduct, obstructed viaduct, station and suburban. Four channel characteristics involving power delay profile, root mean square (RMS) delay spread, RMS angular spread and Ricean K-factor form the datasets used for model training and testing. Then, a novel propagation scenario identification model is proposed by merging a weighted score based feature fusion method into the long short-term memory (LSTM) neural network. The hyper-parameters of the proposed model such as time window length and numbers of hidden units and layers are determined by autocorrelation analysis and cross-validation. Finally, the model performance is evaluated by focusing on the impact of feature selection, comparison of different feature fusion methods, and computational complexity. The evaluation results show that the proposed model has high identification accuracy but acceptable computational complexity.

Intelligent channel prediction plays a key role in artificial intelligence (AI)-optimized or AI-native communication networks for smart high-speed railways (HSRs). This paper investigates the spatial-temporal prediction of channel state information (CSI) and channel statistical characteristics (CSCs) based on deep-learning (DL) for the future smart HSR communication network. A propagation-graph simulation method is used to generate datasets of CSI and CSCs for massive multiple-input multiple-output (mMIMO) channels in a HSR cutting scenario, and realistic channel measurements are used to validate the datasets. Then, single-step ahead and multi-step ahead prediction problems are formulated with the consideration of both spatial and temporal information hidden in the datasets. By exploiting the temporal and spatial correlations of the HSR mMIMO channel, a novel spatial-temporal channel prediction model that combines the convolutional neural network (CNN) and convolutional long short-term memory (CLSTM) is proposed and called as Conv-CLSTM. Moreover, the hyper-parameters of the Conv-CLSTM model are determined by autocorrelation and similarity analysis and cross-validation. Finally, the performance of the Conv-CLSTM model is evaluated in terms of prediction accuracy and space and time computational complexity, and is compared with classical DL models. The evaluation results show that the proposed model has high prediction accuracy but acceptable computational complexity.

This paper investigates the prediction model based on deep learning for the wireless channel characteristics of massive MIMO systems in high-speed railway (HSR) scenarios. Based on the propagation graph theory, we simulate the massive MIMO channel in a HSR cutting scenario. The datasets of spatial-temporal channel characteristics, involving channel state information, Ricean K-factor, delay spread, and angle spread, are generated for the model training and testing, and two kinds of prediction problem formulations, such as single-step and multi-steps, are designed. By considering both the spatial and temporal correlation properties in HSR massive MIMO channels, a novel channel prediction model that combines the convolutional long short-term memory (CLSTM) and convolutional neural network (CNN) is proposed and called as Conv-CLSTM. The hyperparameters of Conv-CLSTM are determined by comparative experiments and autocorrelation and similarity analysis. According to the performance evaluation, it is showed that the proposed Conv-CLSTM outperforms the other deep learning and machine learning models.

In this study, we consider a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with a mixed analog-to-digital converter (mixed-ADC) and hybrid beamforming architecture, in which antenna selection is applied to achieve intelligent assignment of high- and low-resolution ADCs. Both exact and approximate closed-form expressions for the uplink achievable rate are derived in the case of maximum-ratio combining reception. The impacts on the achievable rate of user transmit power, number of radio frequency chains at a base station, ratio of high-resolution ADCs, number of propagation paths, and number of quantization bits are analyzed. It is shown that the user transmit power can be scaled down inversely proportional to the number of antennas at the base station. We propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, the energy efficiency is investigated, and an optimal tradeoff between the achievable rate and power consumption is discussed. Our results will provide a useful reference for the study of mixed-ADC multi-cell mmWave massive MIMO systems with antenna selection.

Coordinated multipoint (CoMP) and massive multiple-input multiple-output (mMIMO) are two of promising technologies in future intelligent high-speed railway (HSR) communication systems, whose performance is fundamentally determined by the characteristics of cooperative mMIMO channels. This article proposes a dynamic three-dimensional (3-D) wideband geometry-based stochastic model (GBSM) for HSR cooperative mMIMO channels. The proposed GBSM employs a sphere model and two elliptic-cylinder models to describe common and uncommon clusters for different links, and integrates the dynamic cluster evolution in both time and array domains. Statistical properties of the cooperative mMIMO model are investigated, such as multi-link spatial cross-correlation function and sum rate capacity. A corresponding dynamic 3-D wideband simulation model for the HSR cooperative mMIMO channel is also proposed. Finally, numerical results of the statistical properties are analyzed, and the proposed model is validated by realistic HSR channel measurement data, in terms of dual-link spatial cross-correlation and channel capacity. This model is more practical and will be helpful for facilitating the design and performance evaluation of future intelligent HSR communication systems.

In future intelligent high-speed railway (HSR) communication systems, coordinated multipoint (CoMP) and massive multiple-input multiple-output (mMIMO) are two promising technologies. A three-dimensional (3-D) wideband cooperative mMIMO geometry-based stochastic model (GBSM) for HSR channels is proposed in this paper. The proposed GBSM employs a sphere model and two elliptic-cylinder models to describe common and uncommon clusters for different links and integrates the cluster evolution in both time and array domains. Besides, multi-link spatial cross-correlation function (CCF) is derived and investigated. And validation of the multi-link spatial cross-correlation function (CCF) is also analyzed according to the actual measurement data. The model is more practical to facilitate the design and performance evaluation of future intelligent HSR communication systems.

The performance of distributed communication systems is commonly subject to the cross-correlation characteristics of multi-link channels. In this paper, we concentrate on the modeling of multi-link small-scale fading (SSF) channels in high-speed train (HST) communication networks with distributed base stations according to the classical geometry-based stochastic model (GBSM). To appropriately describe a multi-link propagation scenario in the HST communication network, a novel geometrical model considering a line-of-sight component, a single-bounced one-ring model, and a double-bounced ellipse-ring model is proposed. Based on the proposed GBSM, the expression of multi-link channel impulse responses (CIRs) is obtained, and multi-link cross-correlation functions are derived and used for numerical analysis. In addition, realistic channel measurements are conducted in the existing HST long-term evolution (LTE) networks and multi-link CIRs are acquired using a time delay window-based partitioning scheme. Finally, the SSF cross-correlation coefficient is extracted by the multi-link channel data and is used to validate the utility of the proposed model.

The vacuum tube high-speed flying train (high-speed flying train) is a novel rail transportation technology. The maglev train can run with low mechanical friction, low air resistance and low noise mode at ultra-high-speed (over 1000 km/h) in all weather conditions inside the vacuum tube. In this paper, we describe the unique characteristics associated with the wireless communication of high-speed flying train, such as high Doppler frequency shift, metal waveguide effect and extreme frequent handoff. Therefore, the solution of leaky-wave system is selected to effectively suppress the Doppler effect in the vacuum tube. The simulation results show the even field distribution at the observation point far away from the leaky-wave system, and the uniform phrase distribution along the train motion direction, which can eliminate Doppler frequency shift. Since the simulation indicates that the conventional time-varying frequency-selective fading channel is converted into a stationary channel, we proposed an electromagnetic wave refractive lens system, which together with the leaky wave near-field convergence technology can directly achieve the even wireless signal coverage for passengers inside the train. In addition, in order to deal with the extremely frequent handoff, the solution of moving cell is adopted, which can be realized by the Centralized, cooperative, cloud Radio Access Network (C-RAN).

Industrial Internet of Things (IIoT) is one of the most important application scenarios of the fifth generation mobile communication. In IIoT applications, the propagation channel differs significantly from the propagation environment of the typical cell communication system. In this paper, two special propagation cases are considered. The sensors/actuators (together with the RF transceiver) sometimes are equipped on the swinging mechanical robot arms (MRAs) and on moving automated guided vehicles (AGVs), which have never been thoroughly investigated, and hence the wireless channel between the sensor/actuator and control center becomes time-varying when the MRA is working and vehicles are moving. A two-dimensional geometrical model for the MRA and a mathematical model of random Doppler offset for AGVs are provided to depict these Doppler frequency trajectories in the industrial propagation environment. By using the realistic measurement data, these Doppler frequency trajectories are verified and the simulated results show good agreements. The established models are informative to design wireless networks for industrial scenarios.

Channel denoising is of much importance for further channel characterization and fading analysis. In this paper, we proposed a novel method for the wireless channel impulse response (CIR) denoising based on neural network. Discriminating effective signals from the noise is considered as a binary classification problem, which can be resolved by some machine learning methods. The back propagation neural network (BPNN) model with the amplitude and angle information of CIR as input data is implemented to settle the classification problem. What's more, the Fβ-score, which combines both Precision and Recall together, is selected as the evaluation index to assess the classification performance. Compared with the performance of wavelet transform, the proposed method shows a better denoising result at both high and low signal-to-noise ratio (SNR) due to its full utilization of amplitude and angle information, while the BPNN with only amplitude as input data shows the worst result comparing with the other two methods.

It is essential to capture joint channel characteristics in cooperative multi-link systems in order to make realistic performance assessments and channel modeling. This paper investigates the joint statistical properties of large-scale parameters (LSPs) in high-speed railway (HSR) multi-link propagation scenarios. A dedicated long-term evolution (LTE) network-based multi-link channel sounding system is established, which breaks the constraints in HSR measurements and supports simultaneous time-frequency-space measurements for multiple links. To guarantee the effectiveness of multi-link measurements, a time-delay-window-based partition method is used to identify channel impulse responses from different links. Based on the processed multi-link data, the statistics of LSPs, involving shadow fading, K-factor, delay spread, and azimuth spread, is estimated and compared for the two links in a plain viaduct scenario on HSR. Then, auto-correlations and two kinds of cross correlations, such as intra-link correlation and inter-link correlation in the measured scenario, are calculated and analyzed. In addition, a simulation model of joint statistical characteristics for LSPs is proposed and validated according to the obtained measurement results. These results can be used to assess the actual performance of cooperative multi-link systems on HSR, and the proposed model can provide realistic inputs for channel models in HSR multi-link propagation scenarios.

This paper presents the analysis of time-frequency-space dispersion and nonstationarity in high-speed railway (HSR) communication networks with the narrow-strip-shaped coverage mode. A long-term evolution (LTE)-based channel sounding system is used to measure the propagation characteristics in a dedicated HSR LTE network with the echo channel effect (ECE) caused by the same signaling from different remote radio units (RRUs). The measurement data in a rural scenario are partitioned into the cases without and with the ECE. Based on the processed data, root mean square (RMS) delay spread, Doppler spread, and angular spread are analyzed and modeled. In addition, the nonstationarity of the HSR channel is studied focusing on the stationarity interval. The presented results will be useful in non-stationary time-frequency-space channel modeling for narrow-strip-shaped HSR communication networks.

Spatial characteristics of the propagation channel have a vital impact on the application of multi-antenna techniques. This paper analyzes angular characteristics and the spatial correlation for high-speed railway (HSR) channels, based on a novel moving virtual antenna array (MVAA) measurement scheme. The principle of the MVAA scheme is deeply investigated and is further verified by a theoretical geometry-based stochastic model. Using the MVAA scheme, virtual single-input multiple-output (SIMO) channel impulse response data are derived from single-antenna measurements in typical HSR scenarios, involving viaduct, cutting, and station. Based on the SIMO channel data, angle of arrival is extracted according to the unitary estimation of signal parameters by the rotational invariance techniques algorithm, and is compared with the theoretical result. Moreover, power angular spectrum and root mean square (rms) angular spread (AS) are provided, and the rms AS results are statistically modeled and comprehensively compared. In addition, spatial correlation is calculated and analyzed, and a rms AS-dependent spatial correlation model is newly proposed to describe the relationship between the angular dispersion and the spatial correlation. The presented results could be used in multi-antenna channel modeling and will facilitate the assessment of multi-antenna technologies for future HSR mobile communication systems.

This paper presents the analysis of nonstationary characteristics for high‐speed railway (HSR) scenarios, according to passive long‐term evolution‐ (LTE‐) based channel measurements. The measurement data collected in three typical scenarios, rural, station, and suburban, are processed to obtain the channel impulse responses (CIRs). Based on the CIRs, the nonstationarity of the HSR channel is studied focusing on the stationarity interval, and a four‐state Markov chain model is generated to describe the birth‐death process of multipath components. The presented results will be useful in dynamic channel modeling for future HSR mobile communication systems.

In this paper, we investigate the cross-correlation properties of large-scale parameters (LSPs), which are based on the practical data measured along Beijing to Tianjin highspeed railway (HSR) line, in long term evolution (LTE) railway networks. A LTE- based channel detector system, established by the Institute of Broadband Wireless Mobile Communications of Beijing Jiaotong University, is utilized in this work to measure the multi-link channel. After that, by applying our proposed method named time-delay based dynamic partition, characteristics of multi-link channels are extracted from the measured data. Then we evaluate the cross- correlation properties between multiple links in several cells, in which plain scenario and relay coverage scenario are considered. Moreover, the initial idea of using coordinated multipoint transmission (CoMP) to eliminate the interference thoroughly in LTE railway networks is first proposed and discussed in this paper. Results are shown in the end, which can be used to exploit multi-link channel models and to optimize the next- generation HSR communication system.

This paper presents angular characteristics for high-speed railway (HSR) channels, based on virtual single-input multiple-output (SIMO) measurements. A virtual array measurement scheme is investigated and used to generate SIMO channel impulse responses (CIRs) from single-antenna measurements in three typical HSR scenarios, including obstructed viaduct, deep cutting, and open station. According to the multi-antenna data, angle of arrival (AOA) is extracted using the Unitary ESPRIT (Estimation of Signal Parameters by the Rotational Invariance Techniques) algorithm, and is compared to the theoretical result. Power angular spectrum (PAS) and corresponding angular spread (AS) are provided, and the AS results in the three HSR scenarios are modeled and compared. These angular characteristics can fulfill the gap of multi-antenna channel models and provide the reference for the assessment of multi-antenna technologies in HSR scenarios.

The performance of media-based modulation (MBM) systems, where additional information can be conveyed by the indices of the channel states created by RF mirrors, over time-selective channels is investigated. By transforming the MBM system model into a traditional MIMO system model, we first propose a reduced complexity sphere decoder algorithm. Then two channel tracking algorithms, which are based on least mean square adaptive filter and recursive least-squares adaptive filter, are employed in order to combat the performance loss caused by the time-varying channels. Numerical results show that the proposed sphere decoder and these two channel tracking algorithms perform well in MBM systems.

Channel measurements are the precondition for the design of future high-speed railway (HSR) communication systems. Due to the measurement restriction and measurement efficiency issues of applying conventional channel sounders in HSR scenarios, railway network based channel sounding methods are becoming quite attractive. In this paper, we employ long term evolution (LTE) railway network to achieve HSR channel measurements. A novel LTE-based HSR channel sounding system is proposed to enable the collection of time-frequency-space channel data. Field measurements that consider both direct and relay coverage schemes are conducted on Beijing to Tianjin HSR in China. Measurement data are partitioned into singlelink case in which the common channel parameters can be derived and multi- link case in which the correlation between different links can be characterized. Measurement results cover path loss (PL), Ricean K-factor, delay spread, single-link and multi-link spatial correlation, which not only confirm the utility of the proposed system but also provide preliminary channel information available for the study of next generation HSR communication systems.

In this paper, a semi-empirical multiple-input multiple-output (MIMO) channel model is proposed for highspeed railway (HSR) viaduct scenarios. The proposed MIMO model is based on single-input single-output (SISO) wideband channel measurements under a viaduct on Zhengzhou-Xi'an HSR in China and a geometry-based stochastic model (GBSM) composed of a one-ring model and an elliptical model. Environment-related parameters in the GBSM are determined by the measured temporal fading characteristics such as K-factor and Doppler power spectral density (PSD). Close agreement is achieved between the model results and measured data. Finally, a deterministic simulation model is established to perform the analysis of the space-time correlation function, the space-Doppler PSD and the channel capacity. This model is more realistic and is particularly beneficial for the performance evaluation of MIMO systems in HSR environment.

Based on wideband radio channel measurements with a bandwidth of up to 50 MHz at 2.35 GHz in a U-shape cutting environment, we analyze the Ricean K-factor for high-speed railway communications. Three types of the K-factor, consisting of narrowband, wideband and delay K-factor, are extracted according to the measured channel responses by using the channel partitioning and combining method. Due to the rich reflecting and scattering components in the U-shape cutting scenario, the K-factor dramatically changes with the frequency. A distance-based statistical narrowband K-factor model covering the frequency variability is proposed. The channel bandwidth dependent property of the wideband K-factor is observed and then a bandwidth-based statistical wideband K-factor model is developed. Moreover, it is found that the K-factor just exists at the beginning of the delay bins in the deep U-shape cutting scenario. These results are provided for use in system design and channel modeling of high-speed railway communications.

This paper studies a novel broadband wireless channel sounding scheme for High-Speed Railway (HSR) scenarios. The proposed scheme employs the downlink signal of Long Term Evolution (LTE) as the excitation signal to acquire the channel impulse responses (CIR) based on the frequency domain method. Then, channel characteristics such as time delay spread, Doppler frequency feature, spatial correlation and directions of departure and arrival can be obtained by the post-processing algorithms. Finally, the verification is performed by comparing the predefined channel models with the derived results.

Based on the realistic channel measurement on High-Speed Railway (HSR) in viaduct scenarios at 2.35 GHz, the dynamic evolution of multipath components is investigated from the birth-death process point of view. Due to the distinction in the amount of resolvable multipath signals, the channel is divided into five segments and can be completely parameterized by several sets of statistical parameters associated with the type of environment and scenario. Then the four-state Markov chain, describing the birth-death number variation of the detected propagation waves, is employed to specialize the temporal stochastic properties. Furthermore, the steady probabilities and transition probabilities are provided which will facilitate the development and evaluation of wireless communication systems under HSR.