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.

The train-to-ground communication system is of vital importance to the safe and reliable operation of the vacuum tube high-speed train (vactrain). To better design the communication system, a full understanding of the channel characterization is essential and the accurate channel model needs to be investigated. In this paper, we propose a hybrid model of ray-tracing (RT) method and the propagation graph (PG) method based on directive scattering model and diffraction model. The line-of-sight (LoS) component, reflection, scattering, and diffraction components are considered. Based on the hybrid model, the expression of the channel transfer function (CTF) is derived and the power delay profile (PDP) and delay spread are obtained and analyzed. The simulation results show the wireless channel characteristics in vactrain scenarios and provide useful insights for future vactrain communication systems.

Low-Earth orbit (LEO) satellites have been considered essential to future air-space-ground integrated networks. Wireless channels significantly impact the performance of communication systems, especially in terms of large-scale fading characteristics. In this article, we propose a novel geometry-based stochastic channel model (GBSM) for LEO satellite-ground large-scale channels. Propagation probabilities of Line-of-Sight (LoS) links, ground specular links, and building specular links for suburban, urban, dense urban, and high-rise urban in different elevations are computed. The Fresnel zone is utilized to determine whether the signals can arrive at the receiver. The impact of the radio coverage and receiver height on propagation probabilities are considered for each scenario. Based on the derived propagation probabilities, the average path loss is computed. In the simulation section, the results of our model are validated by the Monte Carlo method, and the average path loss is compared with the standard model in 3GPP TR 38.811. The comparison results have good consistency with the standard model. Moreover, our model can be applied in multiple scenarios by adjusting the environment parameters compared with the standard model.

In recent years, 5G new radio (NR) has garnered significant attention for its low-latency capabilities. Leveraging 5G NR in the Internet of Vehicles (IoV), the Uu interface has shown potential to support low-latency cellular Vehicle-to-Everything (C-V2X) applications, which have traditionally been supported by the PC5 interface. To explore this potential, this article presents the design and implementation of two 5G simulation platforms. The first platform is a hardware-in-the-loop (HiL) simulation platform, which facilitates radio access network (RAN) latency testing of C-V2X communications using the Uu interface under both 5G conventional configuration (CC) and enhanced configuration (EC). This includes testing Vehicle-to-Network (V2N), Network-to-Vehicle (N2V), and Vehicle-to-Network-to-Vehicle (V2N2V) latency. To further investigate the latency performance of 5G V2N2V in congested scenarios, a second platform, the software (SW) simulation platform, is designed and implemented. The platform assesses the impact of packet configurations and spectrum resource configurations on the RAN latency of 5G V2N2V communication within the congested scenario configured in this article. Latency results from the HiL and SW simulation platforms demonstrate that by adjusting 5G network configurations, packet configurations, and spectrum resource configurations, V2N, N2V, and V2N2V latency can be reduced to less than 2, 3, and 11 ms, respectively.

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.

Hyperloop, a novel rail transportation technology, can run at a speed of more than 1000 km/h in the vacuum tube scenario. To guarantee the safe and reliable operation of the Hyperloop communication system, a new channel model considering the line-of-sight (LoS), reflection, and scattering components is proposed to investigate the channel characteristics of the Hyperloop scenarios. Based on the geometric relationship and physical propagation mechanism, the expression of channel gain is derived and the channel impulse response (CIR) is calculated. Then, the Doppler spectrum and K factor are analyzed. The simulation results show the wireless channel characteristics in the Hyperloop scenarios and provide useful insights for future Hyperloop communication systems.

As a novel communication pattern, non-reciprocal beams employ narrow-width beams to transmit signals and wide-width beams to receive, which can achieve fast beam alignment quickly. To fully understand the channel characterization, we investigate the mmWave propagation path loss (PL) over this special pattern. First, we present a beam-filtered power angular spectrum (PAS) simulation method based on the 3GPP. Specifically, the main lobe width-related beam gain is used to shape the generated PAS. On this basis, we consider two cases, i.e., perfect beam alignment and misalignment. For the first case, we propose a novel exponential decay PL model instead of the inversely proportional relationship. Furthermore, we verify our proposal based on the measured data of 28GHz and the results show that it yields a smaller fitting root mean square error (RMSE). Regarding the latter case, we find that the beam steering-based additional PL can be regarded as the superposition of a deterministic Gaussian function and a stochastic Gaussian noise. Our findings provide insightful references for the implementation of beamwidth-dependent nonreciprocal beam patterns.

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.

Urban rail transit has become an important way for people to travel. The traditional urban rail transit system has fixed infrastructure, relies on base stations for communication, and has poor network robustness. The ad-hoc network has developed rapidly in recent years due to its high stability. And it can be used in urban rail to improve the performance of communication networks. In this paper, a clustering algorithm based on urban rail in-vehicle ad-hoc networks is proposed. The algorithm includes cluster head selected strategy and low-delay queuing strategy. We introduce the network architecture and algorithm theory in detail, and verify the algorithm performance in terms of end-to-end delay and packet loss rate through simulation. As the result, the algorithm can effectively improve communication efficiency and reliability.

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 paper, we performed a series of measurement campaigns on the wireless electromagnetic noise for two typical industrial welding scenarios. On this basis, we investigate the characterization of the impulsive noise mainly from two aspects, that is, frequency and time domains. To start with, a novel denoising method based on the dynamic threshold is proposed to identify the desirable impulse noise from the background noise. Then, in the frequency domain, we focus on the power distribution of impulsive noise at different frequency bands. Results exhibit a shadow effect with regard to different frequency bands and we characterize it by using a linear function with a Gaussian distribution. Besides, analyses on the power spectrum correlation for different polarization modes and scenarios are also provided. In the time domain, we performed a series of statistical analyses from aspects of pulse amplitude, duration, and elapse interval to characterize the impulsive noise. Furthermore, three empirical distributions are employed to depict the parameters' variation tendency, that is, Cauchy distribution for amplitude, Gamma distribution for pulse duration, and exponential distribution for pulse interval. Finally, a first-order two-states Markov method is proposed to model the industrial noise. Simulation results are proved to be consistent with the actual measured results in terms of the amplitude distribution.

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.

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.

As one of the mainstream technologies of vehicle-to-everything (V2X) communication, Cellular-V2X (C-V2X) provides high reliability and low latency V2X communications. And with the development of mobile cellular systems, C-V2X is evolving from long-term evolution-V2X (LTE-V2X) to new radio-V2X (NR-V2X). However, C-V2X test specification has not been completely set in the industry. In order to promote the formulation of relevant standards and accelerate the implementation of industrialization, the field test and analysis based on LTE-V2X in the industrial park scenario is conducted in this paper. Firstly, key technologies of LTE-V2X are introduced. Then, the specific methods and contents of this test are proposed, which consists of functional and network performance tests to comprehensively evaluate the communication property of LTE-V2X. Static and dynamic tests are required in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios to evaluate network performance. Next, the test results verify that all functions are normal, and the performance evaluation indexes are appraised and analyzed. Finally, it summarizes the whole paper and puts forward the future work.

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.

The Industrial Internet of Things (IIoT), as the typical application of the fifth generation mobile communication (5G) has its complex and special electromagnetic propagation characteristics in harsh industrial environments. The metal obstacles such as machines and mechanical arms in industrial scenarios will have an impact on the path loss of radio waves. The large metal equipment can generate strong mirror reflection and scattering waves which produce additional strong multipath components. Furthermore, the propagation channel will become change with the time due to the mobility of the automated guided vehicle, working people, etc. in industrial space. In this paper, based on the measurement data obtained in a typical industrial factory, the propagation channel is characterized. The path loss exponent and channel envelope in line of sight (LoS) scenarios and a special case that the sensor is in metallic box are respectively investigated. Additionally, the level passing rate (LCR) and the spatial correlation characteristics of time varying channels caused by the moving transport vehicle are investigated at different frequency bands. These results provide the basis for the practical deployment of wireless networks for the IIoT systems.

The Industrial Internet of Things (IIOT) can help the manufacturing enterprises improve the production efficiency, reduce costs and achieve intelligent factory production. In this harsh scenario, however, the electromagnetic noise performs a severe impact on IIOT which uses low-power wireless sensor devices. In this paper, we aim to characterize electromagnetic noise for IIoT systems based on the realistic measurement data collected in an automobile factory. The measurements were conducted in time and frequency domain, respectively. By using the frequency-domain measurement data, we can extract the frequency occupation and power amplitude information of electromagnetic noise between 300 MHz to 3 GHz band. By using the time-domain measurement data, the noise amplitude distribution (NAD) of electromagnetic noise at 315MHz, 779MHz and 916MHz bands are extracted. Finally, a continuous hidden Markov model (CHMM) is used to characterize the time-varying property of electromagnetic noise. These results are beneficial and informative when designing wireless networks for the IIoT.

In this paper, the performance of a two-dimensional (2D) Massive MIMO angle parameter system with a uniform circular array (UCA) topology at base station is studied based on the field measurements in indoor line-of-sight (LOS) scenario at a frequency of 4.45[Formula: see text]GHz. Many spatial parameters are extracted, including angle information and correlation characteristic. It is shown that correlation lies between antenna elements and users. It is illustrated in the envelope correlation coefficient which goes on a downward trend while the antenna spacing is increasing in the measurements.

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.

Massive multiple‐input–multiple‐output (MIMO) is acknowledged as a key technology for the fifth generation of wireless communication systems, theoretically proved to be of higher throughput, better robustness, superior spectrum‐ and energy‐efficiency with a general presumption that the channels tend to pairwise orthogonal with the increasing of the asymmetrical antenna pairs. Recently, more and more researches have cast light on massive MIMO system modelling. However, few investigations have been done on spatial characteristics of massive MIMO systems, especially the systems equipped with more than 100 antennas. This study focuses on the massive MIMO channel spatial characteristics based on indoor line‐of‐sight radio propagation measurement at 1.4725 GHz, and extracts spatial parameters such as angle of arrival, angle of departure and power azimuth spectrum from experimental data. The angles of departure (AODs) show a linear decrease along the linear antenna array, which are in agreement with the simulation of the ray tracing. On the basis of the realistic results of angular spread and the Gaussian density model, the investigation proves that antenna elements are deeply correlated and the envelope correlation coefficients show negative exponential down‐trend with the increasing of antenna spacing in massive MIMO systems.

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.

Massive multiple-input multiple-output (Massive MIMO) is acknowledged as a key technology for the 5th generation (5G) of wireless communication systems, which theoretically proved to be of higher throughput, better robustness, superior spectrum- and energy- efficiency. A reliable and realistic channel model serves is the enabling foundation for practical design and testing of communication systems. In the paper we research on propagation characteristics of Massive MIMO based on the realistic measurement conducted in a typical outdoor scenarios at 1.4725 GHz. Many channel parameters like distribution of multipath component (MPC), correlation bandwidth, and angle of departure (AOD) are investigated. Here uniform linear array (ULA) and uniform circular array (UCA) are employed and measured results are compared. These results reveal the antenna configuration significantly influence the propagation characteristics in real Massive MIMO environment, and provide the basis for the practical deployment of Massive MIMO systems.

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.

This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.

This paper investigates optimal resource allocation scheme for a typical uplink single-cell massive MIMO system over spatially correlated fading channels. To reduce the pilot contamination effect, we first propose the orthogonal user grouping strategy to partition the terminals into serval groups and assume the users in the same group reuse an identical orthogonal pilot sequence. Employing this strategy, we derive the closed-form expression of the achievable rate for the maximum-ratio combining (MRC) receiver. Then the expression is used to pursue a detailed analysis of the optimal resource allocation scheme so that the system energy efficiency can be maximized. It is found that the optimal training duration is identical to the number of user groups and, hence, can be selected dynamically according to the second order statistical information about the user channels. Numerical results are presented to verify our analysis, and show that the optimal resource allocation can provide significant performance gains compared to the traditional orthogonal training scheme in the high SNR regime.

The recent hot research topic of a massive multiple-output (MIMO) has been primarily studied based on the assumption that channel vectors are asymptotically pairwise orthogonal. This condition is not exactly satisfied in practice. Correlation will occur among antenna elements at the base- station antenna array in a dense scattering environment, or in a LOS propagation condition. In this paper, by using the Mellin transform of the eigenvalue distribution, we derive the semi- correlated sum-rate capacity of multi-antenna channels with an arbitrary number of antennas in closed form. Afterwards, we employ two commonly- used correlation models to compare the theoretical closed-form results and the simulated results and the final results show that they match well.

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the fifth generation of wireless communication systems. In previous research, different power loss and shadowing effects on different individual antenna elements have been neglected. When characterizing the channel of a massive MIMO system and investigating the system performance, the large scale attenuation (LSA) over the antenna array has not been previously considered. In this paper, based on our proposed accurate geometrical propagation model, the spectral efficiency (in terms of bits/s/Hz sum-rate) result of maximal ratio combining (MRC) detection is investigated. From the simulation results, we find that the sum-rate performance for the MRC detection of our proposed more realistic channel model exceeds the results using the conventional model (where the LSA effect is not included). This proposed model LSA model is beneficial and informative for the research, design, and evaluation of the next generation of wireless communication systems employing massive MIMO configurations.

A new approach based on conventional propagation graph channel modeling was proposed to haracterize the wireless channel in non-light of sight (NLOS) tunnel scenarios. The scattering points are regarded as several points sets, which are different from the propagation-graph theory, then the transfer probability among sets is introduced to adjust the channel impulse response (CIR) taps. The advantage of the proposed method is that wideband channel coefficients, CIR in delay, antennas' correlation coefficient, angle of arrival (AOA), angle of departure (AOD), channel capacity can be calculated analytically for these environments. The validation of the proposed method is performed by the reasonable distribution of the CIR taps, AOD and AOA. Finally some works are done to investigate the variation of tunnel channel coefficients when tunnel bending angle varies, and channel matrix degradation is adopted to explain it.

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.

The plane wave assumption has been used extensively in wireless channel modeling for simplicity. However, when the plane wave model is applied to the massive multiple input and multiple output (MIMO) channel characterization, it is no longer suitable. In this paper, by using the geometrical channel parameterizations, the phase shift difference caused by the spherical wave for a large linear antenna array is investigated, and the Far Region Boundary of a Linear Massive Antenna has been proposed as a criterion to determine whether the user terminal is within the near field of this large antenna structure. Finally, by using ray-tracing method, the proposed boundary within which the propagation caused phase difference exceeds 22.5 is verified. The spherical wave model is necessary for the more accurate channel characterization.

Massive multiple input and multiple output (MIMO) systems can increase the spectrum and energy efficiency of existing cells, and because of this, massive MIMO has been considered as a potential technique for next generation wireless communication networks. Since a thorough knowledge of the propagation channel is a prerequisite of reliable communication systems, massive MIMO channels are of great current interest. In this paper, based on realistic measurements in a stadium scenario in two frequency bands, the stationarity of three basic channel parameters is investigated by using the reverse arrangements test. The results show that channel behaviors in our higher frequency band are stationary over the linear antenna array, whereas this appears untrue at the low frequency band. This non-stationarity phenomenon in the line of sight propagation environment is mainly caused by the stronger reflection and smaller path loss at the low frequency band, which allows more and stronger multipath components, and this leads to substantial channel changes over the large size antenna array.

The non-stationary properties based on Markov chains are proposed to describe the wireless propagation mechanism of High Speed Railway (HSR) under viaduct scenarios. This Markov modeling method reveals the statistical behaviors of the persistence process corresponding to a resolvable multipath component. Based upon the channel measurement on Beijing-Tianjin HSR at 2.35 GHz, the transition probability matrix and the steady-state probability matrix of Markov chains are specified. These proposed model parameters are informative for link-level simulation and prototype verification for HSR communication systems. In addition, the non-stationary properties are first investigated by medium-scale fading entropy and run length to evaluate the degrees of activity and persistence, respectively. Finally, our Markov models are compared with the experimental results by the Kullback-Leibler (KL) distance to obtain the degree of approximation, which show that the second order model provides a good match to the measured data.

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.

For the design and performance evaluation of broadband wireless communication systems in High-Speed Railway (HSR) environments, it is of crucial importance to have accurate and realistic propagation channel model. Based on real measurement data in U-Shape Groove (USG) scenarios at 2.35 GHz on Zhengzhou-Xi'an (ZX) HSR in China, the channel fading characteristics such as path loss, shadowing, K factor, time dispersivity and Doppler effects are specialized. These technical guidelines will promote the development of the wireless communication system under HSR.

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.

The paper describes the measurement campaigns for the broadband channel properties under the high- speed condition, which have been carried out on Zhengzhou to Xi'an (ZX) High-Speed Railway and Beijing to Tianjin (BT) High-Speed Railway. WCDMA with the bandwidth of 3.84MHz is employed as the excitation signal that is transmitted from the base station along the railway and received by the TSMQ by ROHDE & SCHWARZ inside the train. Different scenarios including plain, U-shape cutting, station and hilly terrain are chosen in the measurements and the parameters about the channel multipath properties are extracted, analyzed and briefly reported here. These results are informative for the system designers in the future wireless communication of High-Speed Railway.

This paper presents a novel and practical study on the position-based radio propagation channel for High-Speed Railway by performing extensive measurements at 2.35 GHz in China. The specification on the path loss model is developed. In particular, small scale fading properties such as K-factor, Doppler frequency feature and time delay spread are parameterized, which show dynamic variances depending on the train location and the transceiver separation. Finally, the statistical position-based channel models are firstly established to characterize the High-Speed Railway channel, which significantly promotes the evaluation and verification of wireless communications in relative scenarios.

Doppler spread is particularly detrimental to wireless communication performance especially for transmitting a long packet. In this case, the packet experiences rapidly varying fading compared with transmitting over the static channel. To deal with this problem, the uniform linear antenna (ULA) was proposed to compensate Doppler spread. The ULA consists of a linear antenna array which can be used as space samplers to estimate a virtual point that does not move during packet transmission but without the diversity gain. In this paper, we propose the optimal time diversity position with the ULA compensator over the time-varying Rayleigh fading channel. During transmission, this scheme not only can keep the channel quasi-static but also achieve the optimal time diversity by setting the virtual diversity receiving points at zeros of the time autocorrelation function. When combined with the orthogonal space time block code (STBC), it is proved that this scheme performs even better than that over the static channel with Monte Carlo simulation.