Geometry-based Stochastic Channel Model Research Articles

A General 3-D Geometry-Based Stochastic Channel Model for B5G mmWave IIoT

A General 3-D Geometry-Based Stochastic Channel Model for B5G mmWave IIoT

A Novel Geometry-Based Stochastic Channel Model in Integrated Sensing and Communication Scenarios

A Novel Geometry-Based Stochastic Channel Model in Integrated Sensing and Communication Scenarios

Characterization of Low-Latency Next-Generation eVTOL Communications: From Channel Modeling to Performance Evaluation

Next-generation wireless communication networks are expected to offer extremely high data rates supported by very low latency and radically new applications, which require a new wireless radio technology paradigm. However, it is crucial to assist the radio link over the fast varying and highly dynamic channel to satisfy the diverse requirements of next-generation wireless networks. Recently, communication via autonomous electric vertical takeoff and landing (eVTOL) has gained momentum, owing to its potential for cost-effective network deployment. It is considered one of the most promising technologies conceived to support smart radio terminals. However, to provide efficient and reliable communications between ground base stations and eVTOLs as well as between eVTOLs and other eVTOLs, realistic eVTOL channel models are indispensable. In this paper, we propose a nonstationary geometry-based stochastic channel model for eVTOL communication links. The proposed eVTOL channel model framework considers time-domain nonstationarity and arbitrary eVTOL trajectory and is sufficiently general to support versatile C bands. One of the critical challenges for eVTOL is the fast vertical takeoff and landing flight patterns affecting the regular propagation communication channel. Moreover, we present a new method for estimating the SNR over the non-stationary fast dynamic time-variant eVTOL channel by utilizing the sliding window adaptive filtering technique. Furthermore, we present an information–theoretic approach to characterize the end-to-end transmission delay over the eVTOL channel and prove that the optimal transmission scheme strongly depends upon the eVTOL link configuration. In addition, to analyze the occurrence of deep fade regions in eVTOL links, we analyze the outage probability, which is an important performance metric for wireless channels operating over dynamic fading channels, and make an important observation that the outage probability increases non-linearly with the eVTOL height. Furthermore, we consider the commercially available eVTOL specifications and data to validate the channel model and analyze the Doppler shift and latency for the exponential acceleration and exponential deceleration velocities profiles during the takeoff and landing operation. This paper provides a new and practical approach for the design, optimization, and performance evaluation of future eVTOL-assisted next-generation wireless communications.

A Non-Kronecker Structured PFS Method for Channel Emulation in Multiprobe OTA Setups

The realistic channel environment can be characterized by a geometry-based stochastic channel (GBSC) model composed of multiple clusters, each of which consists of many rays. For the GBSC model, the power angle of arrival (AoA) spectrum of a cluster depends on the power angle of departure-Doppler (AoD-Doppler) spectrum. Hence, this model is non-Kronecker structured. In over-the-air (OTA) testing, the prefaded signal synthesis (PFS) method is valid for emulating the GBSC to measure devices under test (DUTs). However, the emulated channel under the conventional PFS method is a Kronecker case due to the independently and identically distributed fading sequences between probes when synthesizing clusters. It will lead to inaccurate link characteristics for emulation. Therefore, a non-Kronecker structured PFS (NKSPFS) method is proposed. Specifically, the rays mapped to probes are individually weighted to ensure that fading sequences between probes are independent but not identically distributed. To determine the ray weights without degrading marginal correlations for emulation, the constraints are introduced on the deviation between the target spatial-temporal correlation and the emulated spatial-temporal correlation according to the emulated spatial correlation and temporal correlation. Simulation results show that the proposed NKSPFS method outperforms the conventional PFS method in OTA testing of multi-antenna devices.

Joint Channel Parameter Estimation and Scatterers Localization

In this paper, the novel extended space-alternating generalized expectation-maximization (SAGE) algorithm, providing joint propagation channel multi-path component (MPC) estimation and scatterer localization underlying spherical-wavefront multipath model, is proposed. Two geometry-based models are aided for estimating the first and last hop scatterers under different bouncing orders. The performance of the proposed algorithm, as called geometry-aided SAGE (GA-SAGE), is illustrated by means of the Cramér-Rao lower bound derived for parameter estimates and the root-mean-square-estimation-errors (RMSEEs) obtained through and Monte-Carlo simulations, which shows the applicability both near- and far-field estimation. Finally, the GA-SAGE is applied to processing the experimental data obtained from measurements in an indoor office environment by using a single-input multiple-output (SIMO) configuration. The obtained results show that the method proposed outperforms the traditional SAGE algorithm in terms of MPCs estimation accuracy, convergence rate, and the extra capability of localizing scatterers involved in different bouncing order propagation paths. It is considered useful in environment sensing alike applications and makes the GA-SAGE an efficient and effective tool for the development of geometry-based stochastic channel models capable of reproducing channel realizations of the so-called spatial consistency or spatial non-stationarity, which are the basis for the design of transmission technologies using extremely-large antenna array (ELAA) for 5G and beyond.

Improvement of the Cluster-Level Spatial Consistency of Channel Simulator With Reference Points Transition Method

Spatial consistency is a basic physical characteristic of the wireless channel because the neighboring spatial positions share similar clusters, resulting in the spatial correlations of both large-scale parameters (LSPs) and small-scale parameters (SSPs). Although the existing geometry-based stochastic channel model (GSCM)-based channel simulator try to realize the spatial consistency of the channel parameters by the correlated map method or the sum-of-sinusoids (SOS) method, such a stochastic approach also results in that the change of cluster positions calculated by the SSPs appears considerable even in closely neighboring spatial positions, which violates the wide-sense stationary (WSS) conditions over a short distance. To further improve the cluster-level spatial consistency, we investigate the pattern of change of the root mean square (RMS)-delay spread (DS) and derive the continuity of RMS-DS in the entire space. Taking the continuity of RMS-DS as the optimization goal, we determine the change of clusters of the channel, i.e., cluster appearance and disappearance, when the spatial position of mobile terminal (MT) changes and propose the reference points (RPs) transition method. Specifically, we firstly choose the RPs in the target area according to the correlation distance, then generate the channels of RPs by the SOS method stochastically, and finally obtain the channel of MT through the transitions between the RPs deterministically. Simulation results demonstrate that the channel simulator with the proposed RPs transition method can achieve the spatial consistency of LSPs, SSPs, and clusters in the entire 2D or 3D space.

Throughput Multiplexing Efficiency for High-Order Handset MIMO Antennas

In multipath environments, multiple-input multiple-output (MIMO) terminals typically suffer from non-zero correlations due to limited handset space and power imbalances due to different antenna efficiencies or partial hand blockage. These antenna–channel impairments can lead to significant throughput performance degradation of MIMO terminals. The multiplexing efficiency was proposed to quantitatively assess this performance degradation. Previous work only derived the analytical expression of the throughput-based multiplexing efficiency of two-port MIMO antennas for user equipment (UE). However, the fifth-generation (5G) UE dictates four or more antennas. In this paper, we extend the multiplexing efficiency metric to high-order MIMO UEs. Both correlation-based channel models and geometry-based stochastic channel models were used for validation. Besides dipole antennas, two representative terminal antennas were also employed for simulation verifications in this work.

Three-Dimensional Geometry-Based Stochastic Channel Modeling for Intelligent Reflecting Surface-Assisted UAV MIMO Communications

In this letter, a three-dimensional (3D) geometry-based stochastic multiple-input multiple-output (MIMO) channel model for intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications is proposed. The IRS is distributed on the surface of buildings to assist the UAV transmitter to reflect its signals to the receiver on the ground level and to improve the communication quality by passive beamforming at the IRS. In such a channel model, the signals transmitted from the UAV transmitter will first get scattered by the clusters between the UAV and IRS before arriving at the IRS, and subsequently arrive at the ground receiver. We separate this channel model into a subchannel model between the UAV and the IRS, as well as a subchannel model between the IRS and the receiver. Based on the derivations of complex channel coefficients for the above two subchannel models, we investigate the impact of IRS on the underlying propagation channel by studying the spatial cross-correlation functions (CCFs). Simulation results are provided to show the statistical propagation characteristics of the IRS-assisted UAV MIMO channels.

Geometry-Based Channel Modelling for Vehicle-to-Vehicle Communication: A Review

Vehicle-to-Vehicle (V2V) communication has received a lot of attention over recent years since it can improve the efficiency and safety of roads for drivers and travellers besides other numerous applications. However dynamic nature of this environment makes it difficult to come up with a suitable wireless communication channel model that can be used in the simulation of any V2V communication system. This paper reviews the recent techniques used for geometry-based stochastic channel modelling for V2V communication. It starts by presenting the various classes of wireless communication channel models available in the literature with more emphasis on the Geometry-Based Stochastic channel Models (GBSM). Then the paper discusses in more detail the state of the art of the regular-shaped and irregular-shaped GBSM for the two- and three-dimensional models. Finally, main challenges are identified and future research directions in this area are recommended.

Real-Time Vehicular Wireless System-Level Simulation

Future automation and control units for advanced driver assistance systems (ADAS) will exchange sensor and kinematic data with nearby vehicles using wireless communication links to improve traffic safety. In this paper we present an accurate real-time system-level simulation for multi-vehicle communication scenarios to support the development and test of connected ADAS systems. The physical and data-link layer are abstracted and provide the frame error rate (FER) to a network simulator. The FER is strongly affected by the non-stationary doubly dispersive fading process of the vehicular radio communication channel. We use a geometry-based stochastic channel model (GSCM) to enable a simplified but still accurate representation of the non-stationary vehicular fading process. The propagation path parameters of the GSCM are used to efficiently compute the time-variant condensed radio channel parameters per stationarity region of each communication link during run-time. Five condensed radio channel parameters mainly determine the FER forming a parameter vector: path loss, root mean square delay spread, Doppler bandwidth, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -factor, and line-of-sight Doppler shift. We measure the FER for a pre-defined set of discrete grid points of the parameter vector using a channel emulator and a given transmitter-receiver modem pair. The FER data is stored in a table and looked up during run-time of the real-time system-level simulation. We validate our methodology using empirical measurement data from a street crossing scenarios demonstrating a close match in terms of FER between simulation and measurement.

Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than 3.6 dB, 78 ns, and 52 Hz, respectively, for 80% of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for 86% of the simulation duration.

Geometry-Cluster-Based Stochastic MIMO Model for Vehicle-to-Vehicle Communications in Street Canyon Scenarios

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

Map-Based Millimeter-Wave Channel Models: An Overview, Data for B5G Evaluation and Machine Learning

Within the mm-Wave range of the B5G communication systems, there will appear many types of applications with different link types. In discussions on how to cover the various channel modeling requirements of such applications, some researchers have suggested map-based channel models that are based on a ray-tracing algorithm. This article thus begins with an overview of mapbased mm-Wave channel models. The overview includes available modeling requirements with map-based channel models and the categorization of map-based channel parameters. We then explain why map-based channel models are necessary for researchers trying to evaluate novel technologies in the mm-Wave range. They are particularly necessary when the technologies operate with a new link type or when channel behaviors of a user exhibit user-specific characteristics. Next, we share the measurement data and the map-based channel parameters, which can model new link types and have user-specific characteristics. Finally, as a use case of the proposed channel model, we evaluate a machine-learning-based beam-selection algorithm that exploits power delay profiles through the shared database and a geometry-based stochastic channel model (GSCM). The delay profiles of the shared database are user-specific so they are highly correlated with angular parameters. Numerical results show that the algorithm can be more accurately evaluated through the shared database than through a GSCM. We expect that our overview and sharing the database will enable researchers to readily design a map-based channel model and evaluate B5G and machine-learning technologies.

Computationally Efficient Millimeter-Wave Backscattering Models: A Single-Scattering Model

The use of millimeter-wave (mm-wave) frequency bands for fifth-generation (5G) cellular mobile communications has led to intense interest from academia and industry over these spectrum resources. Despite extensive measurement campaigns and channel modeling efforts, there is a lack of deterministic backscattering models addressing the impact of the size and orientation of static scatterers on the radio channel. In this article, two 3-D computationally efficient models for calculating backscattering based on the Fresnel integrals and the error function are proposed and validated both against simulations and measurements. In addition, applying the same methodology, state-of-the-art mm-wave blockage (forward-scattering) models are modified to capture backscattered fields. Furthermore, both the introduced and the modified models preserve the structure of geometry-based stochastic channel models (GSCMs) and thus their implementation in system-level simulators is substantially beneficial due to their good accuracy and short computation time.

A 2-D Geometry-Based Stochastic Channel Model for 5G Massive MIMO Communications in Real Propagation Environments

Spherical wavefront and array nonstationarity due to physically large antenna array are two typical characteristics specific to massive multiple-input–multiple-output (M-MIMO) propagation. Unfortunately, traditional MIMO channel models need to be enhanced to better characterize these two new propagation features. In light of this limitation, this article proposes an enhanced two-dimensional geometry-based stochastic channel model for 5G M-MIMO communications in real propagation environments. Specifically, a coordinate-based channel model framework is first introduced. Based on this, the impact of spherical wavefront can be mathematically described with the aid of some spatial geometric manipulations. Moreover, a general Markov process is developed to model the array nonstationarity characteristic due to cluster appearance and disappearance along the array. Meanwhile, the associated approach to derive the state transition probabilities of the proposed Markov process is detailed. Finally, capabilities of the proposed channel model in characterizing spherical wavefront and array nonstationarity of M-MIMO channels are shown by the numerical simulations. Moreover, validity of our proposal is verified by the measurement data.

The COST IRACON Geometry-Based Stochastic Channel Model for Vehicle-to-Vehicle Communication in Intersections

Vehicle-to-vehicle (V2V) wireless communications can improve traffic safety at road intersections and enable congestion avoidance. However, detailed knowledge about the wireless propagation channel is needed for the development and realistic assessment of V2V communication systems. We present a novel geometry-based stochastic MIMO channel model with support for frequencies in the band of 5.2–6.2 GHz. The model is based on extensive high-resolution measurements at different road intersections in the city of Berlin, Germany. We extend existing models, by including the effects of various obstructions, higher order interactions, and by introducing an angular gain function for the scatterers. Scatterer locations have been identified and mapped to measured multi-path trajectories using a measurement-based ray tracing method and a subsequent RANSAC algorithm. The developed model is parameterized, and using the measured propagation paths that have been mapped to scatterer locations, model parameters are estimated. The time variant power fading of individual multi-path components is found to be best modeled by a Gamma process with an exponential autocorrelation. The path coherence distance is estimated to be in the range of 0–2 m. The model is also validated against measurement data, showing that the developed model accurately captures the behavior of the measured channel gain, Doppler spread, and delay spread. This is also the case for intersections that have not been used when estimating model parameters.

A Geometric Scattering Model for Circularly Polarized Indoor Channels

Wireless systems are being deployed in evermore complex environments, ones that can introduce multipath resulting in not only severe frequency selective fading but also signal depolarization. Polarization diversity is a known technique to mitigate such fades. To date, primarily linearly polarized (LP) systems have been used for indoor communications, but recently circularly polarized (CP) systems have received attention. As such there is motivation to better understand how both linearly and CP systems perform in depolarizing environments. In this article, we present a 3-D geometry-based stochastic channel model (GBSCM) for dual-polarized systems to evaluate circular and LP in cluttered settings. Using a Poisson distribution, the model generates a number of multipath scatterers. Measurements have been conducted at 2.4 GHz to validate the model. Leveraging parameters extracted from measured data, linear and circular polarization conditions are simulated for both line-of-sight (LOS) and non-LOS (NLOS) conditions. cross-polarization discrimination (XPD) and 1% link margins are considered as performance metrics. These metrics applied to both simulated and empirical data are both within 1 dB of each other.

Antenna Correlation Under Geometry-Based Stochastic Channel Models

Antenna correlation is an important measure for designing multiple-input–multiple-output (MIMO) antenna systems. A lower antenna correlation indicates a better MIMO performance that can be achieved with the underlying antenna systems. In the antenna design community, it is very common to evaluate the antenna correlation with isotropic or nonisotropic (e.g., Gaussian-distributed) angular power spectrum (APS) as baselines. On the other hand, antenna correlation can also be evaluated via channel transfer function (CTF) under the given propagation channel, e.g., drawn from the bidirectional geometry-based stochastic channel model. In this letter, the analytic forms for the antenna correlation based on the APS and the CTF are derived, respectively, with their similarities and differences explained. Moreover, a numerical example is also given with a standard channel model to support our findings.

A Study of Dynamic Multipath Clusters at 60 GHz in a Large Indoor Environment

The available geometry-based stochastic channel models (GSCMs) at millimetre-wave (mmWave) frequencies do not necessarily retain spatial consistency for simulated channels, which is essential for small cells with ultra-dense users. In this paper, we work on cluster parameterization for the COST 2100 channel model using mobile channel simulations at 61 GHz in Helsinki Airport. The paper considers a ray-tracer which has been optimized to match measurements, to obtain double-directional channels at mmWave frequencies. A joint clustering-tracking framework is used to determine cluster parameters for the COST 2100 channel model. The KPowerMeans algorithm and the Kalman filter are exploited to identify the cluster positions and to predict and track cluster positions respectively. The results confirm that the joint clustering-and-tracking is a suitable tool for cluster identification and tracking for our ray-tracer results. The movement of cluster centroids, cluster lifetime and number of clusters per snapshot are investigated for this set of ray-tracer results. Simulation results show that the multipath components (MPCs) are grouped into clusters at mmWave frequencies.

Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions

A low complexity massive multiple-input multiple-output (MIMO) technique is studied with a geometry-based stochastic channel model, called COST 2100 model. We propose to exploit the discrete-time Fourier transform of the antenna correlation function to perform user scheduling. The proposed algorithm relies on a trade off between the number of occupied bins of the eigenvalue spectrum of the channel covariance matrix for each user and spectral overlap among the selected users. We next show that linear precoding design can be performed based only on the channel correlation matrix. The proposed scheme exploits the angular bins of the eigenvalue spectrum of the channel covariance matrix to build up an "approximate eigenchannels" for the users. We investigate the reduction of average system throughput with no channel state information at the transmitter (CSIT). Analysis and numerical results show that while the throughput slightly decreases due to the absence of CSIT, the complexity of the system is reduced significantly.