Spatial Channel Model Research Articles

Modular CSI Quantization for FDD Massive MIMO Communication

We consider high-dimensional MIMO transmissions in frequency division duplexing (FDD) systems. For precoding, the frequency selective channel has to be measured, quantized and fed back to the base station by the users. When the number of antennas is very high this typically leads to prohibitively high quantization complexity and large feedback. In 5G New Radio (NR), a modular quantization approach has been applied for this, where first a low-dimensional subspace is identified for the whole frequency selective channel, and then subband channels are linearly mapped to this subspace and quantized. We analyze how the components in such a modular scheme contribute to the overall quantization distortion. Based on this analysis we improve the technology components in the modular approach and propose an orthonormalized wideband precoding scheme and a sequential wideband precoding approach which provide considerable gains over the conventional method. We compare the performance of the developed quantization schemes to prior art by simulations in terms of the projection distortion, overall distortion and spectral efficiency, in a scenario with a realistic spatial channel model.

UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model

Unmanned Aerial Vehicles (UAVs) have become crucial components of building air–ground integrated networks for sixth-generation mobile communications. UAV-related technology has advanced quickly in both military and civilian contexts. Problems with long-distance and dynamic communication need to be fixed in the non-terrestrial transmission channels of high-altitude UAVs. For long-distance UAV dynamic non-terrestrial transmission channel modeling, a spatial statistical channel model–ray tracing (SSCM-RT) hybrid channel model is proposed and verified, and SSCM-RT channel simulations incorporating spatial consistency analysis are carried out to realize the description of the communication process in the dynamic scenario of the non-terrestrial transmission in symmetric and asymmetric situations. The SSCM-RT model can output channel state information continuously, which is helpful for the quantitative description and characterization of the UAV channel in practical settings.

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels.

This paper studies blind source separation (BSS) for frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over (i) line-of-sight (LOS), (ii) single-cluster, and (iii) multiple-cluster Spatial Channel Model (SCM) settings. The sources are stationary, spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by leveraging direction of arrival (DOA) information through an FH estimation stage, a DOA estimation stage, and a pairing stage with the latter associating FH patterns with physical sources via their estimated DOAs. Current methods in the literature do not perform the association of multiple frequency hops to the sources they are transmitted from. We bridge this gap by pairing the FH estimates with DOA estimates and labeling signals to their sources, irrespective of their hopped frequencies. A state filtering technique, referred to as hidden state filtering (HSF), is developed to refine DOA estimates for sources that follow a HMM. Numerical results demonstrate that the proposed approach is capable of separating multiple intermittent FH sources.

Measurement-Based 3-D Channel Modeling With Cluster-of-Scatterers Estimated Under Spherical-Wave Assumption

With the rapid development of the sixth generation (6G) communication systems, the extremely large antenna arrays (ELAAs) have attracted substantial attention in both academia and industry. As the array size increases, the so-called “near-field” effect, i.e. channel parameters varying with respect to the antenna’s position becomes evident. Conventional channel models fail to describe such effects as the parameters of multipath components (MPCs) specified in these models are fixed regardless of array configuration. Due to the mismatch between the constant MPC assumption and the actual variant behavior observed, the parameterization of the models aiming to describe the spatial non-stationary channels cannot be conducted effectively. In order to mitigate the model mismatch and reproduce the channels with spatial non-stationarity specifically for ELAA applications, in this work, a novel measurement-based stochastic channel modeling method is proposed and applied to establishing Scatterer-based Spatial Channel Model (SSCM) that can be used to unify the far- and near-field models using spherical wave and generate the spatial non-stationary channels for ELAAs by integrating the traditional spatial channel model (SCM) with “clusters-of-scatterers (CoS)”. To establish the models based on measurements, the locations of first- and last-hop scatterers existing along propagation paths in a channel are estimated based on the spherical-wave assumption. A novel clustering algorithm is used to group the MPCs taking into account those scatterers’ locations. With a channel measurement campaign conducted using a 32×32 Rx planar antenna array at carrier frequency of 10 GHz, exemplary SSCMs are established. The SSCM is capable of reproducing the parts of the environment influencing wave propagation, and therefore can be used for the research of integrated sensing and communication (ISAC) applications.

Deep Learning Based Predictive Beamforming Design

This paper investigates deep learning techniques to predict transmit beamforming based on only historical channel data without current channel information in the multiuser multiple-input-single-output downlink. This will significantly reduce the channel estimation overhead and improve the spectrum efficiency especially in high-mobility vehicular communications. Specifically, we propose a joint learning framework that incorporates channel prediction and power optimization, and produces prediction for transmit beamforming directly. In addition, we propose to use the attention mechanism in the Long Short-Term Memory Recurrent Neural Networks to improve the accuracy of channel prediction. Simulation results using both a simple autoregressive process model and the more realistic 3GPP spatial channel model verify that our proposed predictive beamforming scheme can significantly improve the effective spectrum efficiency compared to traditional channel estimation and the method that separately predicts channel and then optimizes beamforming.

Path loss models for outdoor environment—with a focus on rain attenuation impact on short-range millimeter-wave links

The deployment of a millimeter wave over a short path is one of the keys to enabling technologies for the next generation of wireless communication systems. Path loss (PL) is the most important parameter to indicate the performance of the mm-wave wireless channel. However, the accuracy and efficiency of each model are limited to characterize path loss for an environment that is different in terms of weather conditions and geographical arrangement from that for which they have been designed. This paper analyzed path loss for accurate signal estimation in Malaysia based on outdoor microcellular at 38GHz on a 300 m path length. The impact of rain attenuation on path loss, path loss exponent (PLE), and shadow fading (SF) have been investigated. This paper also presents two-channel models utilized for simulations in terms of the outdoor Large-Scale Path Loss, the statistical spatial channel model NYUSIM (version 2) developed in 2019 by New York University (NYU) and the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14 channel mode. Even though the CI and 3GPP models are accurate and suitable in the area where the measurement campaign was carried out in the temperate climate and must need modification for different regions, such as tropical climate. The underestimation can be interpreted because of the difference in AF's attenuation factors (pressure, humidity, temperature, rain rate) calculated by the CI model in the NYUSIM simulations and the attenuation factor (AF) obtained from measurement data. The NYUSIM channel model better estimated the measured data of path loss compared with 3GPP. Thus, the CI model is suitable for outdoor environments.

Spatial Non-Stationary Near-Field Channel Modeling and Validation for Massive MIMO Systems

Existing deraining methods focus mainly on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera. LFIs are becoming popular in the computer vision and graphics communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel method, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. To make full use of the LFI, it adopts 4D convolutional layers to simultaneously process all sub-views of the LFI. In the pipeline, the rain detection network, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via computing pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI.

CAW: A Remote-Sensing Scene Classification Network Aided by Local Window Attention.

Remote-sensing image scene data contain a large number of scene images with different scales. Traditional scene classification algorithms based on convolutional neural networks are difficult to extract complex spatial distribution and texture information in images, resulting in poor classification results. In response to the above problems, we introduce the vision transformer network structure with strong global modeling ability into the remote-sensing image scene classification task. In this paper, the parallel network structure of the local-window self-attention mechanism and the equivalent large convolution kernel is used to realize the spatial-channel modeling of the network so that the network has better local and global feature extraction performance. Experiments on the RSSCN7 dataset and the WHU-RS19 dataset show that the proposed network can improve the accuracy of scene classification. At the same time, the effectiveness of the network structure in remote-sensing image classification tasks is verified through ablation experiments, confusion matrix, and heat map results comparison.

OTA Throughput Prediction of MIMO Antennas for Wireless Devices by Simulated Realistic Channel Model

A cost-effective and accurate method for predicting over-the-air (OTA) data throughput of multiple-input and multiple-output (MIMO) wireless devices in the antenna design stage is proposed. The method incorporates passive characteristics of MIMO antennas, realistic channel model, and conductive measurement results in a computer simulation framework. With extracted relation between received power and signal-to-noise ratio (SNR) of a wireless device at antenna ports by a conductive test and measured passive characteristics of MIMO antennas, OTA throughput versus received power in a realistic channel model is obtained by the Monte Carlo simulation of Shannon’s law. The unique features of this method include: no channel emulator equipment is required; a wide variety of precoding schemes can be applied; passive characteristics of antennas are fully incorporated; and frequency-selective fading channel can be adopted. The method has been intensively validated by comparing the calculated OTA throughputs with those measured by a multiprobe anechoic chamber method under spatial channel model extension (SCME) in three aspects: spatial correlation, radiation efficiency, and mutual coupling, showing as low as 0.2–0.3 dB discrepancy in power level in most cases. The method can be used to evaluate a MIMO array antenna design of a wireless device with respect to OTA performance.

Capacity-Oriented Envelope Correlation Coefficient for Multiple Antennas of Mobile Devices

In this article, a capacity-oriented envelope correlation coefficient (ECC) that incorporates a preferred channel model for assessing a multiple antenna system of a multiple-input-multiple-output (MIMO) mobile device is proposed. Originated from the rudimentary law of ergodic channel capacity, the ECC for multiple antennas (ECC-M) is proven to holistically reflect the impact of radiation patterns and total efficiencies of multiple antennas on channel capacity. The closed-form ECC-M expressions for Rayleigh fading channel model and ray-based spatial channel model are studied, leading to an easy-to-use ECC-M expression that approximately incorporates a realistic channel model. The correlation of the proposed ECC-M to the channel capacity is demonstrated through the Monte Carlo (MC) simulation of three four-element MIMO antenna testbeds in two channel models, showing that the antenna total efficiency and a realistic channel model involved in the ECC-M play an important role in assessing the performance of multiple antennas. It is justified that the difference in channel capacities of two multiple antenna systems is highly relevant to their ECC-Ms at high signal-to-noise ratios (SNRs). The proposed ECC-M is also experimentally validated through over-the-air (OTA) throughput measurement, revealing that the ECC-M can effectively assess the OTA performance of multiple antenna systems for MIMO terminal devices.

A Framework of Mahalanobis-Distance Metric With Supervised Learning for Clustering Multipath Components in MIMO Channel Analysis

As multipath components (MPCs) are experimentally observed to appear in clusters, cluster-based channel models have been focused in the wireless channel study. However, most of the MPC clustering algorithms for MIMO channels with delay and angle information of MPCs are based on the distance metric that quantifies the similarity of two MPCs and determines the preferred cluster shape, greatly impacting MPC clustering quality. In this paper, a general framework of Mahalanobis-distance metric is proposed for MPC clustering in MIMO channel analysis, without user-specified parameters. Remarkably, the popular multipath component distance (MCD) is proved to be a special case of the proposed distance metric framework. Furthermore, two machine learning algorithms, namely, weak-supervised Mahalanobis metric for clustering and supervised large margin nearest neighbor, are introduced to learn the distance metric. To evaluate the effectiveness, a modified channel model is proposed based on the 3GPP spatial channel model to generate clustered MPCs with delay and angular information, since the original 3GPP spatial channel model (SCM) is incapable to evaluate clustering quality. Experiment results show that the proposed distance metric can significantly improve the clustering quality of existing clustering algorithms, while the learning phase requires considerably limited efforts of labeling MPCs.

Millimetre wave 3-D channel modelling for next generation 5G networks

<abstract><p>Millimetre wave (mm-wave) spectrum (30-300GHz) is a key enabling technology in the advent of 5G. However, an accurate model for the mm-wave channel is yet to be developed as the existing 4G-LTE channel models (frequency below 6 GHz) exhibit different propagation attributes. In this paper, a spatial statistical channel model (SSCM) is considered that estimates the characteristics of the channel in the 28, 60, and 73 GHz bands. The SSCM is used to mathematically approximate the propagation path loss in different environments, namely, Urban-Macro, Urban-Micro, and Rural-Macro, under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions. The New York University (NYU) channel simulator is utilised to evaluate the channel model under various conditions including atmospheric effects, distance, and frequency. Moreover, a MIMO system has been evaluated under mm-wave propagation. The main results show that the 60 GHz band has the highest attenuation compared to the 28 and 73 GHz bands. The results also show that increasing the number of antennas is proportional to the condition number and the rank of the MIMO channel matrix.</p></abstract>

FPGA Acceleration of 3GPP Channel Model Emulator for 5G New Radio

The channel model is by far the most computing intensive part of the link level simulations of multiple-input and multiple-output (MIMO) fifth-generation new radio (5G NR) communication systems. Simulation effort further increases when using more realistic geometry-based channel models, such as the three-dimensional spatial channel model (3D-SCM). Channel emulation is used for functional and performance verification of such models in the network planning phase. These models use multiple finite impulse response (FIR) filters and have a very high degree of parallelism which can be exploited for accelerated execution on Field Programmable Gate Array (FPGA) and Graphics Processing Unit (GPU) platforms. This paper proposes an efficient re-configurable implementation of the 3rd generation partnership project (3GPP) 3D-SCM on FPGAs using a design flow based on high-level synthesis (HLS). It studies the effect of various HLS optimization techniques on the total latency and hardware resource utilization on Xilinx Alveo U280 and Intel Arria 10GX 1150 high-performance FPGAs, using in both cases the commercial HLS tools of the producer. The channel model accuracy is preserved using double precision floating point arithmetic. This work analyzes in detail the effort to target the FPGA platforms using HLS tools, both in terms of common parallelization effort (shared by both FPGAs), and in terms of platform-specific effort, different for Xilinx and Intel FPGAs. Compared to the baseline general-purpose central processing unit (CPU) implementation, the achieved speedups are 65X and 95X using the Xilinx UltraScale+ and Intel Arria FPGA platform respectively, when using a Double Data Rate (DDR) memory interface. The FPGA-based designs also achieved ~3X better performance compared to a similar technology node NVIDIA GeForce GTX 1070 GPU, while consuming ~4X less energy. The FPGA implementation speedup improves up to 173X over the CPU baseline when using the Xilinx UltraRAM (URAM) and High-Bandwidth Memory (HBM) resources, also achieving 6X lower latency and 12X lower energy consumption than the GPU implementation.

Parametric codebook design for efficient signal transmission using uniform circular arrays

In order to improve the quality of the received signal and system spectral efficiency, accurate beamforming using a given antenna array is essential for multiple-input multiple-output (MIMO) systems. To obtain desired MIMO transmission performance, construction of codebooks which are composed of matching beamforming vectors to the array structure is important. To effectively cover different types of mobile traffic, the base station for 5G new radio employs antenna arrays in various sizes and shapes. Nevertheless, the codebooks adopted by the 3GPP standard so far are based on the uniform linear array and the uniform planar array, necessitating design techniques for a wider class of antenna arrays. In this paper, we propose codebook construction methods for the uniform circular array with parameters to flexibly set the initial phase and step size based on the channel characteristics of the user equipment (UE). When tested over the 3GPP spatial channel model, the proposed codebooks show a substantial amount of gain over the conventional codebooks in all UE locations within the cell.

Millimeter Wave and Sub-Terahertz Spatial Statistical Channel Model for an Indoor Office Building

Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to play a vital role in 6G wireless systems and beyond due to the vast available bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial statistical channel model for mmWave and sub-THz frequencies based on extensive radio propagation measurements at 28 and 140 GHz conducted in an indoor office environment from 2014 to 2020. Omnidirectional and directional path loss models and channel statistics such as the number of time clusters, cluster delays, and cluster powers were derived from over 15,000 measured power delay profiles. The resulting channel statistics show that the number of time clusters follows a Poisson distribution and the number of subpaths within each cluster follows a composite exponential distribution for both LOS and NLOS environments at 28 and 140 GHz. This paper proposes a unified indoor statistical channel model for mmWave and sub-Terahertz frequencies following the mathematical framework of the previous outdoor NYUSIM channel models. A corresponding indoor channel simulator is developed, which can recreate 3-D omnidirectional, directional, and multiple input multiple output (MIMO) channels for arbitrary mmWave and sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna beamwidth. The presented statistical channel model and simulator will guide future air-interface, beamforming, and transceiver designs for 6G and beyond.

Millimeter-Wave Beam Selection in Time-Varying Channels With User Orientation Changes

The use of many narrow beams to overcome the adverse propagation conditions in millimeter-wave channels leads to large training durations and overheads in 5G systems. This causes the beam measurements to become outdated by different extents at the time the transmit and receive beams are selected. The rapid changes in user device orientation exacerbate this problem. We first present a novel modified bivariate Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> (MBN) model to tractably and accurately characterize the joint, non-stationary statistics of the channel gains seen at the times of measurement and data transmission. We then derive a novel and optimal beam selection rule that maximizes the average rate of the system. We use the MBN model to propose a near-optimal, practically amenable bound-based selection (PABS) rule. Our approach captures several pertinent aspects about the spatial channel model and 5G, such as transmission of periodic bursts of reference signals, feedback from the user to enable the base station to select its transmit beam, and the faster pace of updating the data rate compared to the transmit-receive beam pair. The PABS rule markedly outperforms the widely used conventional power-based selection rule and is less sensitive to user orientation changes.

Simulation and Analysis of Extended Spatial Channel Model in Vehicle-to-Vehicle Communication Environments

In this paper, an extension spatial channel model (SCM) for vehicle-to-vehicle (V2V) communications is proposed. To efficiently illustrate the real-world scenarios and reflect nonstationary properties of V2V channels, all effective scattering objects are subdivided into three categories of clusters according to the relative position of clusters. Besides, a birth-death process is introduced to model the appearance and disappearance of clusters on both the array and time axes. Their impacts on V2V channels are investigated via statistical properties including correlation functions. Additionally, a closed-form expression of channel impulse response (CIR) is derived from an extension SCM and cluster-based models. Furthermore, the spatial and frequency statistical properties of the reference model are thoroughly investigated. Finally, simulation results show that the proposed SCM V2V model is in close agreement with previously reported results, thereby validating the accuracy and effectiveness of the proposed model.

Evaluating the Need For An Analytical Model of Correlated mm-Wave Channels of 5G For Mobility-Related Scenarios

Communication with the millimeter-wave (mm-Wave) is one of the new features present in the fifth generation (5G) mobile networks. The possibility and challenges related to a large population have encouraged many study-based studies to evaluate proposed solutions. Communication in millimeter waves is one of the following Great innovations of the 5G mobile network. For a big reason: Available bandwidth value. However, a millimeter-wave channel given the nature of harsh distribution and sensitivity to congestion. Therefore, the correct modeling of mm-Wave channels is the basis for obtaining accurate results in the simulation of the mm-Wave mobile system. The quality of the mm-wave simulation depends on the channel model, but the favorite channel models of millimeter waves such as the spatial channel model (SCM) have advanced computer density and reduce the density of the solution. End-to-end, systematic development and research. This paper gives an informative exercise in the mm-Wave full-grown section included in ns-3 open source testing system. The module incorporates a variety of models as well as the ability to combine real-time measurements or radiation tracking data. This paper provides equal distribution of a few measurements, for example, SINR in very big cases. We also provide pieces of information on the use of cases where using an easy model can produce the official outcome.

Hybrid Neural Network-Based Fading Channel Prediction for Link Adaptation

We propose an artificial intelligence-based channel prediction scheme that can potentially facilitate link adaptation in customized communication systems. Link adaptation is a key process for wireless communication that requires accurate channel state information (CSI). However, the CSI may be outdated because of computational and propagation delays. In addition, a subframe with no CSI reference signal cannot provide CSI feedback. The proposed scheme solves these problems by predicting future channels. Although traditional stochastic methods suffer from marginal prediction accuracy or unacceptable computational complexity, neural networks allow time series prediction for channels even considering constraints for practical application. We introduce a hybrid architecture for improving the prediction accuracy of the neural network when extracting meaningful features. The proposed scheme uses a single hybrid network that can predict channels in different environments. Simulations were performed using a spatial channel model to evaluate the performance at the system-level, and the results indicated that the proposed scheme effectively increases the prediction accuracy for the channel quality indicator and spectral efficiency.

Modeling Multi-Frequency Characteristics for Classroom and Hall Scenarios at 2-4, 9-11 and 27-29 GHz Bands

This paper investigates the wideband channel characteristics obtained in a classroom and a hall indoor scenarios at 2-4, 9–11 and 27-29GHz. A virtual uniform circular array (UCA) based channel sounding system was utilized to capture wideband spatial channel characteristics. The propagation parameters of multipath components (MPCs) were estimated by using a high resolution parameter estimation (HRPE) algorithm. The estimated MPCs are further grouped into clusters via a novel clustering identification algorithm based on the KPowerMeans algorithm. The comparison of the composite and cluster-level characteristics at multiple frequency bands in both scenarios is investigated. Moreover, the impact of the indoor room environment, i.e. dimension and furniture, on the propagation channel is also analyzed. The statistics of channel parameters at multiple frequency bands extracted constitute a stochastic clustered spatial channel model.