Statistical Channel Modeling for Indoor VLC Communications Based on Channel Measurements

Visible light communication(VLC)has attracted much attention in the research of sixthgeneration(6G)systems.Furthermore,channel modeling is the foundation for designing efficient and robust VLC systems.In this paper,we present extensive VLC channel measurement campaigns in indoor environments,i.e.,an office and a corridor.Based on the measured data,the large-scale fading characteristics and multipath-related characteristics,including omnidirectional optical path loss(OPL),K-factor,power angular spectrum(PAS),angle spread(AS),and clustering characteristics,are analyzed and modeled through a statistical method.Based on the extracted statistics of the above-mentioned channel characteristics,we propose a statistical spatial channel model(SSCM)capable of modeling multipath in the spatial domain.Furthermore,the simulated statistics of the proposed model are compared with the measured statistics.For instance,in the office,the simulated path loss exponent(PLE)and the measured PLE are 1.96and 1.97,respectively.And,the simulated medians of AS and measured medians of AS are 25.94°and 24.84°,respectively.Generally,the fact that the simulated results fit well with measured results has demonstrated the accuracy of our SSCM.

Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything Communications

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Degree of Freedom Analysis for Holographic MIMO Based on a Mutual-Coupling-Compliant Channel Model

Degree of freedom(DOF)is a key indicator for spatial multiplexing layers of a wireless channel.Traditionally,the channel of a multiple-input multiple-output(MIMO)half-wavelength dipole array has a DOF that equals the antenna number.However,recent studies suggest that the DOF could be less than the antenna number when strong mutual coupling is considered.We utilize a mutual-coupling-compliant channel model to investigate the DOF of the holographic MIMO(HMIMO)channel and give a upper bound of the DOF with strong mutual coupling.Our numerical simulations demonstrate that a dense array can support more DOF per unit aperture as compared with a half-wavelength MIMO system.

Analytical Modeling and Mechanism Analysis of Time-Varying Excitation for Surface Defects in Rolling Element Bearings

Surface defects,including dents,spalls,and cracks,for rolling element bearings are the most common faults in rotating machinery.The accurate model for the time-varying excitation is the basis for the vibration mechanism analysis and fault feature extraction.However,in conventional investigations,this issue is not well and fully addressed from the perspective of theoretical analysis and physical derivation.In this study,an improved analytical model for time-varying displacement excitations(TVDEs)caused by surface defects is theoretically formulated.First and foremost,the physical mechanism for the effect of defect sizes on the physical process of rolling element-defect interaction is revealed.According to the physical interaction mechanism between the rolling element and different types of defects,the relationship between time-varying displacement pulse and defect sizes is further analytically derived.With the obtained time-varying displacement pulse,the dynamic model for the deep groove bearings considering the internal excitation caused by the surface defect is established.The nonlinear vibration responses and fault features induced by surface defects are analyzed using the proposed TVDE model.The results suggest that the presence of surface defects may result in the occurrence of the dual-impulse phenomenon,which can serve as indexes for surface-defect fault diagnosis.

Neural Network and GBSM Based Time-Varying and Stochastic Channel Modeling for 5G Millimeter Wave Communications

In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Stress wave analysis of high-voltage pulse discharge rock fragmentation based on plasma channel impedance model

High-voltage pulse discharge(HVPD)rock fragmentation controls a plasma channel forming inside the rock by adjusting the electrical parameters,electrode type,etc.In this work,an HVPD rock fragmentation test platform was built and the test waveforms were measured.Considering the effects of temperature,channel expansion and electromagnetic radiation,the impedance model of the plasma channel in the rock was established.The parameters and initial values of the model were determined by an iterative computational process.The model calculation results can reasonably characterize the development of the plasma channel in the rock and estimate the shock wave characteristics.Based on the plasma channel impedance model,the temporal and spatial distribution characteristics of the radial stress and tangential stress in the rock were calculated,and the rock fragmentation effect of the HVPD was analyzed.

Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.

3D Geometry-Based UAV-MIMO Channel Modeling and Simulation

A more general narrowband regular-shaped geometry-based statistical model(RS-GBSM) combined with the line of sight(LoS) and single bounce(SB) rays for unmanned aerial vehicle(UAV) multiple-input multiple-output(MIMO) channel is proposed in this paper. The channel characteristics, including space-time correlation function(STCF), Doppler power spectral density(DPSD), level crossing rate(LCR) and average fade duration(AFD), are derived based on the single sphere reference model for a non-isotropic environment. The corresponding sum-of-sinusoids(SoS) simulation models including both the deterministic model and statistical model with finite scatterers are also proposed for practicable implementation. The simulation results illustrate that the simulation models well reproduce the channel characteristics of the single sphere reference model with sufficient simulation scatterers. And the statistical model has a better approximation of the reference model in comparison with the deterministic one when the simulation trials of the stochastic model are sufficient. The effects of the parameters such as flight height, moving direction and Rice factor on the characteristics are also studied.

Channel Modeling and Performance Analysis for UAV Relay Systems

In this paper, a multi-hop relay channel model based on unmanned aerial vehicles(UAVs) is established by taking into account of the propagation loss, shadowing, and multi-path fading. Based on the proposed channel model, the cascaded propagation loss of relay link and the cascaded probability density function(PDF) of channel fading are derived. Moreover, the theoretical performance of the UAV-based relay system, i.e., the outage probability, bit error rate(BER), and channel capacity, is also analysed and derived. Simulation results show agreement with theoretical results for the hill, mountain, and sea scenarios, indicating the accuracy of both the simulations and derivations.

Channel Measurement and Path Loss Modeling from 220 GHz to 330 GHz for 6G Wireless Communications

Terahertz(THz)communication has been envisioned as a key enabling technology for sixthgeneration(6G).In this paper,we present an extensive THz channel measurement campaign for 6G wireless communications from 220 GHz to 330 GHz.Furthermore,the path loss is analyzed and modeled by using two single-frequency path loss models and a multiplefrequencies path loss model.It is found that at most frequency points,the measured path loss is larger than that in the free space.But at around 310 GHz,the propagation attenuation is relatively weaker compared to that in the free space.Also,the frequency dependence of path loss is observed and the frequency exponent of the multiple-frequencies path loss model is 2.1.Moreover,the cellular performance of THz communication systems is investigated by using the obtained path loss model.Simulation results indicate that the current inter-site distance(ISD)for the indoor scenario is too small for THz communications.Furthermore,the tremendous capacity gain can be obtained by using THz bands compared to using microwave bands and millimeter wave bands.Generally,this work can give an insight into the design and optimization of THz communication systems for 6G.

Marine Mobile Wireless Channel Modeling Based on Improved Spatial Partitioning Ray Tracing

In 5G era,it is expected to achieve wireless network coverage including offshore areas.Modeling of marine wireless channels is the basis of constructing a marine communication system.In this paper,a communication scene between an unmanned aerial vehicle(UAV)and a boat is simulated to study the marine wireless channel.Firstly,an improved spatial partitioning ray tracing algorithm is proposed to track the propagation path of electromagnetic waves at sea surface.Secondly,a mobile channel is simulated and modeled based on the track results.Finally,a loss measurement is carried out in the coastal waters based on the simple wireless channel loss measuring platform,and a path loss propagation model is built.Then we compare the actual measurement data with the simulation results and find that the two are have good consistency,which further verifies the reliability of the simulation.

V2V Channel Characterization and Modeling for Underground Parking Garages

As an important part of the intelligent transportation system(ITS),vehicle-to-vehicle(V2V)communication will improve the safety and efficiency of the transportation system by realizing the information transmission between vehicles.The underground parking garage is a typical scenario in V2V communication,but as an indoor environment,it is different from the conventional outdoor road scenarios significantly.Therefore,the purpose of this paper is to analyze and model the V2V channel characteristics of an underground garage.In this paper,a novel channel measurement carried out in the underground garage is first introduced.Considering that there are a lot of obstacles in an underground garage,the non-line-of-sight(NLoS)condition is taken into account during the measurement.Then,channel characteristic parameters,including large-scale fading,delay spread,and K-factor,are analyzed based on the measured data.In addition,we also carry out channel angle of arrival analysis,show the distribution of azimuth of arrival(AOA)and elevation of arrival(EOA)of multipath components(MPC),and conduct a statistical analysis of the angular spread.In the process of analysis and modeling,we focus on the differences between line-of-sight(LoS)and NLoS conditions.The analysis and conclusions presented in this paper will enrich the understanding of V2V channel and benefit the design of V2V communications.

Finite-State Markov Wireless Channel Modeling for Railway Tunnel Environments

In recent years,high-speed railways(HSRs)have developed rapidly with a high transportation capacity and high comfort level.A tunnel is a complex high-speed rail terrain environment.It is very important to establish an accurate channel propagation model for a railway tunnel environment to improve the safety of HSR operation.In this paper,a method for finite-state Markov chain(FSMC)channel modeling with least squares fitting based on non-uniform interval division is proposed.First,a path loss model is obtained according to measured data.The communication distance between the transmitter and receiver in the tunnel is non-uniformly divided into several large non-overlapping intervals based on the path loss model.Then,the Lloyd-Max quantization method is used to determine the threshold of the signal-to-noise ratio(SNR)and the channel state quantization value and obtain the FSMC state transition probability matrix.Simulation experiments show that the proposed wireless channel model has a low mean square error(MSE)and can accurately predict the received signal power in a railway tunnel environment.

Time-varying gravity field model of Sichuan-Yunnan region based on the equivalent mass source model

High-precision time-varying gravity field is an effective way to study the internal mass movement and understanding the spatio-temporal evolution process of the geodynamic system.Compared to the satellite gravity measurement,the repeated terrestrial gravity observation can provide a more high-order signal related to the shallow crust and subsurface.However,the suitable and unified method for gravity model estimation is a key problem for further applications.In this study,we introduce the spherical hexahedron element to simulate the field source mass and forward model the change of gravity field located at the Sichuan-Yunnan region(99—104°E,23—29°N)in the four epochs from 2015 to 2017.Compared to the experimental results based on Slepian or spherical harmonics frequency domain method,this alternative approach is suitable for constructing the equivalent mass source model of regional-scale gravity data,by introducing the first-order smooth prior condition of gravity time-varying signal to suppress the high-frequency component of the signal.The results can provide a higher spatial resolution reference for regional gravity field modeling in the Sichuan-Yunnan region.

UWB Channel Modeling and Simulation with Continuous Frequency Response

Ultra-wideband(UWB)technology is a prospective technology for high-rate transmission and accurate localization in the future communication systems.State-of-art channel modeling approaches usually divide the UWB channel into several sub-band channels and model them independently.By considering frequency-dependent channel parameters,a novel analytical UWB channel model with continuous frequency response is proposed.The composite effect of all frequency components within the UWB channel on the channel impulse response(CIR)of delay domain is derived based on the continuous channel transfer function(CTF)of frequency domain.On this basis,a closed-form simulation model for UWB channels and geometry-based parameter calculation method are developed,which can guarantee the continuity of channel characteristics on the frequency domain and greatly reduce the simulation complexity.Finally,the proposed method is applied to generate UWB channel with 2 GHz bandwidth at sub-6GHz and millimeter wave(mmWave)bands,respectively.The channel measurements are also carried out to validate the proposed method.The simulated CIR and power gain are shown to be in good agreement with the measurement data.Moreover,the comparison results of power gain and Doppler power spectral density(DPSD)show that the proposed UWB channel model Received:Apr.23,2022 Revised:Jun.09,2022 Editor:Wei Fan achieves a good balance between the simulation accuracy and efficiency.