Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO

Millimeter-wave massive multiple-input-multiple-output (mmWave mMIMO) is reported as a key enabler in the fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. Exploiting this sparsity enables the applicability of hybrid precoding and achieves pilot reduction. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. A motivation for the Fourier character of this transformation is the fact that the steering response vectors in antenna arrays are Fourier basis vectors. Still, a Fourier transformation is not necessarily the optimal one, due to many reasons. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace. Since the dictionary is obtained by training over actual channel measurements, this transformation is shown to yield two immediate advantages. First, is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second, is improving the channel representation quality, and thus reducing the underlying power leakage phenomenon. Consequently, this allows for both improved channel estimation and facilitated beam selection in mmWave mMIMO. This is especially the case when the antenna array is not perfectly uniform. Besides, a learned dictionary is also used as the precoding operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and the beam selectivity, thereby improving the spectral efficiency.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A rare case of posttraumatic bilateral orbital myositis in a young boy — a case report

Orbital myositis (OM) is a benign inflammatory disease of the orbit characterised by a polymorphous lymphoid infiltrate with varying degrees of fibrosis, without a known local or systemic cause. In this paper, we present a case of a young boy who sustained a trauma to his eyes a few days prior to admission, after which he developed bilateral orbital pain and ocular motility limitation. He underwent the appropriate investigations including orbital imaging and blood laboratory workup, which were all consistent with a diagnosis of posttraumatic bilateral orbital myositis. He was treated with steroids for few weeks, and when the dose of steroids was tapered, he had a relapse of the same disease with a different presentation, which was later controlled with a higher dose of steroids, after which the patient went into remission

The Clinical and Nonclinical Values of Nonexercise Estimation of Cardiovascular Endurance in Young Asymptomatic Individuals

Exercise testing is associated with barriers prevent using cardiovascular (CV) endurance (CVE) measure frequently. A recent nonexercise model (NM) is alleged to estimate CVE without exercise. This study examined CVE relationships, using the NM model, with measures of obesity, physical fitness (PF), blood glucose and lipid, and circulation in 188 asymptomatic young (18–40 years) adults. Estimated CVE correlated favorably with measures of PF (r = 0.4 − 0.5) including handgrip strength, distance in 6 munities walking test, and shoulder press, and leg extension strengths, obesity (r = 0.2 − 0.7) including % body fat, body water content, fat mass, muscle mass, BMI, waist and hip circumferences and waist/hip ratio, and circulation (r = 0.2 − 0.3) including blood pressures, blood flow, vascular resistance, and blood (r = 0.2 − 0.5) profile including glucose, total cholesterol, LDL-C, HDL-C, and triglycerides. Additionally, differences (P < 0.05) in examined measures were found between the high, average, and low estimated CVE groups. Obviously the majority of these measures are CV disease risk factors and metabolic syndrome components. These results enhance the NM scientific value, and thus, can be further used in clinical and nonclinical settings

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promising for this application. This approach models city regions as nodes in a transportation graph and their relations as edges. GNNs utilize local node features and the graph structure in the prediction. However, more efficient forecasting can still be achieved by following two main routes; enlarging the scale of the transportation graph, and simultaneously exploiting different types of nodes and edges in the graphs. However, both approaches are challenged by the scalability of GNNs. An immediate remedy to the scalability challenge is to decentralize the GNN operation. However, this creates excessive node-to-node communication. In this paper, we first characterize the excessive communication needs for the decentralized GNN approach. Then, we propose a semi-decentralized approach utilizing multiple cloudlets, moderately sized storage and computation devices, that can be integrated with the cellular base stations. This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization. Also, we propose a heterogeneous GNN-LSTM algorithm for improved taxi-level demand and supply forecasting for handling dynamic taxi graphs where nodes are taxis. Extensive experiments over real data show the advantage of the semi-decentralized approach as tested over our heterogeneous GNN-LSTM algorithm. Also, the proposed semi-decentralized GNN approach is shown to reduce the overall inference time by about an order of magnitude compared to centralized and decentralized inference schemes.Comment: 13 pages, 10 figures, LaTeX; typos corrected, references added, mathematical analysis adde

Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results

Delineation of Copper Mineralization Zones at Wadi Ham, Northern Oman Mountains, United Arab Emirates Using Multispectral Landsat 8 (OLI) Data

© Copyright © 2020 Howari, Ghrefat, Nazzal, Galmed, Abdelghany, Fowler, Sharma, AlAydaroos and Xavier. Copper deposits in the ultramafic rocks of the Semail ophiolite massifs is found to be enormous in the region of northern Oman Mountains, United Arab Emirates. For this study, samples of copper were gathered from 14 different sites in the investigation area and were analyzed in the laboratory using the X-ray diffraction, GER 3700 spectroradiometer, and Inductively Coupled Plasma-Mass Spectrometer. Detection and mapping of copper-bearing mineralized zones were carried out using different image processing approaches of minimum noise fraction, principal component analysis, decorrelation stretch, and band ratio which were applied on Landsat 8 (OLI) data. The spectra of malachite and azurite samples were characterized by broad absorption features in the visible and near infrared region (0.6–1.0 µm). The results obtained from the principal component analysis, minimum noise fraction, band ratio, decorrelation stretch, spectral reflectance analyses, and mineralogical and chemical analyses were found to be similar. Thus, it can be concluded that multispectral Landsat 8 data are useful in the detection iron ore deposits in arid and semi-arid regions

Multivariate statistical analysis of urban soil contamination by heavy metals at selected industrial locations in the Greater Toronto area, Canada

A good understanding of urban soil contamination with metals and the location of pollution sources due to industrialization and urbanization is important for addressing many environmental problems. The results are reported here of an analysis of the metals content in urban soils samples next toindustrial locations in the Greater Toronto Area (GTA) in Ontario, Canada. Theanalyzed metals are Cr, Mn, Fe, Ni, Cu, Zn, and Pb. Multivariate geostatistcalanalysis (correlation matrix, cluster analysis, principal component analysis) is used to estimate soil chemical content variability. The correlation matrix exhibits a positive correlation with Mn, Fe, Cu, Zn, Cd, and Pb. The principal component analysis (PCA) displays two components. The first component explains the major part of the total variance and is loaded heavily with Cr, Mn, Fe, Zn,and Pb, and the sources are industrial activities and traffic flows. The second component is loaded with Ni, and Cd, and the sources could be lithology andtraffic flow. The results of the cluster analysis demonstrate three major clusters: 1) Mn-Zn, 2) Pb-Cd-Cu and Cr, 3) Fe-Ni. The geo-accumulation index and the pollution load index are determined and show the main I geovalues to be in the range of 0-1.67; the values indicate that the soil samples studied for industrial locations in the GTA are slightly to moderately contaminated with Cr, Fe, Cu, Zn, and Cd, and moderately contaminated with Pb,while Ni, and Mn fall in class "0". Regarding the pollution load ingindex (PLI), the lowest values are observed at stations 6, 7, 9, 10, 11, 12,25, 27 and 28, while the highest values are recorded for stations 1, 5, 6, 13,14, 16, 17, 18, 20, 22 and 24, and very high PLI readings are seen for stations 5, 13, 16, 17, 18, 22 and 24. These data confirm that the type of industries, especially metallurgical and chemical related ones, in the study area, in addition to high traffic flows, are the main sources for soil pollution in the GTA

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p&lt;0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p&lt;0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Deep learning-based optimal ris interaction exploiting previously sampled channel correlations

The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multi-layer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm

FDD massive MIMO downlink channel estimation via selective sparse coding over AOA/AOD cluster dictionaries

Sparse coding over a redundant dictionary has recently been used as a framework for downlink channel estimation in frequency division duplex massive multiple-input multiple-output antenna systems. This usage allows for efficiently reducing the inherently high training and feedback overheads. We present an algorithm for downlink channel estimation via selective sparse coding over multiple cluster dictionaries. A channel training set is divided into clusters based on the angle of the arrival/departure of the majority physical subpaths corresponding to each channel tap. Then, a compact dictionary is trained in each cluster. Channel estimation is done by first identifying the channel cluster and then using its dictionary for reconstruction. This selective sparse coding allows for adaptive regularization via sparse model selection, thereby offering additional regularization to the ill-posed channel estimation problem. We empirically validate the selectivity of the cluster dictionaries. Simulation results show the advantage of the proposed algorithm in achieving better estimation quality at lower computational cost, as compared the case of using standard sparse coding.IEE