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 $K$ 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.

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

Objective: This study is to evaluate the anti-obese effects of glucosamine (GLC) and chitosan oligosaccharide (COS) on high-fat diet-induced obese rats. Methods: The rats were randomly divided into twelve groups: a normal diet group (NF), a high-fat diet group (HF), Orlistat group, GLC high-, middle-, and low-dose groups (GLC-H, GLC-M, GLC-L), COS1 (COS, number-average molecular weight ≤1000) high-, middle-, and low-dose groups (COS1-H, COS1-M, COS1-L), and COS2 (COS, number-average molecular weight ≤3000) high-, middle-, and low-dose groups (COS2-H, COS2-M, COS2-L). All groups received oral treatment by gavage once daily for a period of six weeks. Results: Rats fed with COS1 gained the least weight among all the groups (P < 0.01), and these rats lost more weight than those treated with Orlistat. In addition to the COS2-H and Orlistat groups, the serum total cholesterol (CHO) and low-density lipoprotein cholesterol (LDL-C) levels were significantly reduced in all treatment groups compared to the HF group (P < 0.01). The various doses of GLC, COS1 and COS2 reduced the expression levels of PPARγ and LXRα mRNA in the white adipose tissue. Conclusions: The results above demonstrated that GLC, COS1, and COS2 improved dyslipidemia and prevented body weight gains by inhibiting the adipocyte differentiation in obese rats induced by a high-fat diet. Thus, these agents may potentially be used to treat obesity.

https://www.mdpi.com/1660-3397/13/5/2732/pdf

Anti-Obese Effect of Glucosamine and Chitosan Oligosaccharide in High-Fat Diet-Induced Obese Rats

Previous studies examining daily temperature and stroke incidence have given conflicting results. We undertook this retrospective study of all stroke admissions in those aged 35 years old and above to Hong Kong public hospitals from 1999 through 2006 in order to better understand the effects of meteorological conditions on stroke risk in a subtropical setting. We used Poisson Generalized Additive Models with daily hemorrhagic (HS) and ischemic stroke (IS) counts separately as outcomes, and daily mean temperature, humidity, solar radiation, rainfall, air pressure, pollutants, flu consultation rates, day of week, holidays, time trend and seasonality as predictors. Lagged effects of temperature, humidity and pollutants were also considered. A total of 23,457 HS and 107,505 IS admissions were analyzed. Mean daily temperature had a strong, consistent, negative linear association with HS admissions over the range (8.2-31.8°C) observed. A 1°C lower average temperature over the same day and previous 4 days (lags 0-4) being associated with a 2.7% (95% CI: 2.0-3.4%, P < .0.0001) higher admission rate after controlling for other variables. This association was stronger among older subjects and females. Higher lag 0-4 average change in air pressure from previous day was modestly associated with higher HS risk. The association between IS and temperature was weaker and apparent only below 22°C, with a 1°C lower average temperature (lags 0-13) below this threshold being associated with a 1.6% (95% CI:1.0-2.2%, P < 0.0001) higher IS admission rate. Pollutant levels were not associated with HS or IS. Future studies should examine HS and IS risk separately.

Weather, season, and daily stroke admissions in Hong Kong

Wideband large-scale array systems operating at millimeter-wave bands are expected to play a key role in future communication systems. It is recommended by standardization groups to use spherical-wave models (SWMs) to characterize the channel in near-field cases because of the large array apertures and the small cell size. However, this feature is not widely reflected in channel models yet, mainly due to the high computational complexity of SWMs compared to that of the conventional plane-wave model (PWM), especially when ultrawideband signals are considered. In this paper, a maximum likelihood estimator of low computational complexity is implemented with an SWM for ultrawideband signals. The measurement data obtained from an ultrawideband large-scale antenna array system at 28–30 GHz are processed with the proposed algorithm. The power azimuth-delay profiles estimated from the SWM and the PWM are compared to those obtained from rotational horn antenna measurement, respectively. It shows that the multipath components (MPCs) are well-estimated with the proposed algorithm, and significant improvement in estimation performance is achieved with the SWM compared to the PWM. Moreover, the physical interpretation of the estimated MPCs is also given along with the estimated scatterers.

/pdf/channel-characterization-for-wideband-large-scale-antenna-4mqd49q1jc.pdf

Channel Characterization for Wideband Large-Scale Antenna Systems Based on a Low-Complexity Maximum Likelihood Estimator

This communication deals with the grouping of the multipath radio channel components. The estimation of the number of clusters that a given data set exhibits is an important step in channel modeling and parameterization. We present a performance assessment of several cluster validity indices using Monte Carlo simulations. Synthetic channels were generated by means of the extended Saleh-Valenzuela model and used as input to a clustering algorithm which outputs several solutions with different number of clusters. The cluster validity indices allow the selection of the best solution. Finally, an example is presented where the best performing indices were applied to an experimental data set.

Estimation of the Number of Clusters in Multipath Radio Channel Data Sets

Next generations of satellite communication systems demand the use of high frequency bands and the effect of the troposphere at these frequencies has a major impact in the signal attenuation. In this context, the objective of the work presented in this paper is to take advantage of the Weather Research and Forecasting (WRF) system, capable of providing reanalysis and forecasts at high resolution, to be able to simulate the atmospheric conditions, to finally estimate the signal attenuation for slant path satellite links beyond 10 GHz. This is of paramount importance to effectively supplant the lack of measurements for the design of new satellite systems.

http://ieeexplore.ieee.org/iel7/6526699/6546197/06546505.pdf

Use of WRF model to characterize propagation effects in the troposphere

This paper is about a few implementation aspects of a clustering algorithm for grouping parameters of the channel's multipath components. Issues as initialization and the computation of distance between multipath components are discussed. Special attention is given to the cluster validation problem dealing with the number of clusters' estimation. In this context, some validation measures and fusion techniques are described. Synthetic data generated by the Extended Saleh-Valenzuela model was used as input for the clustering algorithm. Results from the clustering algorithm and validation measures are presented.

Clustering of the multipath radio channel parameters

This paper is about a few implementation aspects of a clustering algorithm for grouping parameters of the channel's multipath components. Issues as initialization and the computation of distance between multipath components are discussed. Special attention is given to the cluster validation problem dealing with the number of clusters' estimation. In this context, some validation measures and fusion techniques are described. Synthetic data generated by the Extended Saleh- Valenzuela model was used as input for the clustering algorithm. Results from the clustering algorithm and validation measures are presented. I. INTRODUCTION Research, design and analysis of advanced wireless communications systems require a detailed understanding of the electromagnetic wave propagation phenomena in mobile radio environments. System performance evaluation yields representative results only if the underlying channel models reflect the most relevant features of the physical channel. An

Maura Outeiral Garcia

Papers

Use of WRF model to characterize propagation effects in the troposphere

Clustering of the multipath radio channel parameters

Clustering of the