Ruisi He

On the Clustering of Radio Channel Impulse Responses Using Sparsity-Based Methods

Radio channel modeling has been an important research topic, as the analysis and evaluation of any wireless communication system requires a reliable model of the channel impulse response (CIR). The classical work by Saleh and Valenzuela and many recent measurements show that multipath component (MPC) arrivals in CIRs appear at the receiver in clusters. To parameterize the CIR model, the first step is to identify clusters in CIRs, and a clustering algorithm is thus needed. However, the main weakness of the existing clustering algorithms is that the specific model for the cluster shape is not fully taken into account in the clustering algorithm, which leads to erroneous clustering and reduced performance. In this paper, we propose a novel CIR clustering algorithm using a sparsity-based method, which exploits the feature of the Saleh-Valenzuela (SV) model that the power of the MPCs is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved using reweighted ℓ1 minimization. Then, a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. Finally, a clustering enhancement approach, which employs the goodness-of-fit (GoS) test to evaluate clustering accuracy, is used to further improve the performance. The proposed algorithm incorporates the anticipated behaviors of clusters into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. Measurements validate the proposed algorithm, and comparisons with other algorithms show that the proposed algorithm has the best performance and a fairly low computational complexity.

Experimental Characterization and Correlation Analysis of Indoor Channels at 15 GHz

The indoor radio channels at 15u2009GHz are investigated based on measurements. The large- and small-scale fading behaviors as well as the delay dispersion characteristics are discussed. It is found that the large-scale fading, Ricean -factor, and delay spread can be described by log-normal distributions. Furthermore, both autocorrelation and cross correlation properties of the above parameters are analyzed and modeled. These parameters characterize fading and delay behaviors as well as their mutual dependency and can be used as empirical values for future wireless system design and simulation in 15u2009GHz short-range indoor channels.

A Kernel-Power-Density-Based Algorithm for Channel Multipath Components Clustering

Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the

RECOME: A new density-based clustering algorithm using relative KNN kernel density

K

Impacts of Absorber Loadings on Simulating the Multipath Channel in a Reverberation Chamber

nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multiple-input-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for cluster-based channel modeling.

Propagation and Wireless Channel Modeling Development on Wide-Sense Vehicle-to-X Communications

Abstract Discovering clusters from a dataset with different shapes, densities, and scales is a known challenging problem in data clustering. In this paper, we propose the RElative COre MErge (RECOME) clustering algorithm. The core of RECOME is a novel density measure, i.e., Relative K nearest Neighbor Kernel Density (RNKD). RECOME identifies core objects with unit RNKD, and partitions non-core objects into atom clusters by successively following higher-density neighbor relations toward core objects. Core objects and their corresponding atom clusters are then merged through α -reachable paths on a KNN graph. We discover that the number of clusters computed by RECOME is a step function of the α parameter with jump discontinuity on a small collection of values. A fast jump discontinuity discovery (FJDD) method is proposed based on graph theory. RECOME is evaluated on both synthetic datasets and real datasets. Experimental results indicate that RECOME is able to discover clusters with different shapes, densities, and scales. It outperforms six baseline methods on both synthetic datasets and real datasets. Moreover, FJDD is shown to be effective to extract the jump discontinuity set of parameter α for all tested datasets, which can ease the task of data exploration and parameter tuning.

Geometrical-Based Modeling for Millimeter-Wave MIMO Mobile-to-Mobile Channels

In reverberation chambers, the multipath channels with different delay characteristics can be generated by loading the chamber with different amounts of absorbers. This paper investigates the impacts of absorber loadings on the delay characteristics based on realistic measurements. An efficient method for estimating the root-mean-square (rms) delay spread in the chamber is presented. Furthermore, it is found that the chamber loadings also significantly affect the quality of a digitally modulated signal and the corresponding modulation quality measurements are performed.

Geometrical-Based Statistical Modeling for Polarized MIMO Mobile-to-Mobile Channels

The need for improving the safety and the efficiency of transportation systems has become of extreme importance. In this regard, the concept of vehicle-to-X (V2X) communication has been introduced with the purpose of providing wireless communication technology in vehicular networks. Not like the traditional views, the wide-sense V2X (WSV2X) communications in this paper are defined by including not only vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications but also train-to-X (T2X) communications constituted of train-to-train (T2T) and train-to-infrastructure (T2I) communications. All the information related to the wide-sense V2X channels, such as the standardization, scenarios, characters, and modeling philosophies, is organized and summarized to form the comprehensive understanding of the development of the WSV2X channels.

Mobility Model-Based Non-Stationary Mobile-to-Mobile Channel Modeling

A geometric multiple-input multiple-output (MIMO) channel model is proposed for millimeter-wave (mmWave) mobile-to-mobile (M2M) applications based on the two-ring reference model, where cluster-based nonisotropic scattering at both ends of the radio link is considered. The proposed model employs a few clusters of scatterers located on two rings centered on the transmitter and receiver, and intracluster azimuth spread of scatterers is further characterized according to mmWave channel characteristics. From the model, the time–frequency correlation function, power delay profile (PDP), and the Doppler power spectrum are derived. By adjusting the cluster number, cluster center position, and the degree of intracluster nonisotropic scattering, the model is adaptable to a variety of mmWave M2M scenarios. Model validation is further conducted by comparing the simulated PDPs with mmWave outdoor measurements. Based on a detailed investigation of channel correlation functions, it is found that a small number of clusters leads to high channel correlation, and the degree of intracluster nonisotropic scattering has major impact on correlation only if cluster number is small. In addition, the Doppler power spectrum is similar to the U-shaped spectrum, and several factors (e.g., small cluster number, high intracluster azimuth spread, and large antenna spacing) introduce significant fluctuations in the Doppler power spectrum. Finally, the model is implemented with directional antennas for safety related M2M scenarios, which leads to high channel correlation compared with using omnidirectional antennas. These observations and conclusions can be considered as a guidance for the mmWave M2M MIMO system design.