scholarly journals Bounding the Practical Error of Path Loss Models

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Caleb Phillips ◽  
Douglas Sicker ◽  
Dirk Grunwald
Keyword(s):  
A Priori ◽  
Path Loss ◽  
Urban Environments ◽  
Production Networks ◽  
Modeling Error ◽  
Propagation Models ◽  
Performance Accuracy ◽  
Rural And Urban ◽  
Standard Models ◽  
Loss Models

We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios wherea prioripredictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.

scholarly journals Impact of engineering parameters on performance of relay-assisted network

2020 ◽  
Vol 20 (1) ◽  
pp. 248
Author(s):  
Issam Maaz ◽  
Jean-Marc Conrat ◽  
Jean-Christophe Cousin ◽  
Samer Alabed
Keyword(s):  
Path Loss ◽  
Relay Node ◽  
Urban Environments ◽  
Full Duplex ◽  
Relay Nodes ◽  
Decode And Forward ◽  
Capacity Enhancement ◽  
Relay Location ◽  
Engineering Parameters ◽  
Loss Models

<span>This paper compares the performance of a relay assisted network to the performance given by a classical macrocell network without the presence of relay node schemes. The capacity enhancement provided by a relaying system as a function of the relay antenna height and the propagation environment surrounding the relay nodes is analyzed and discussed in details. The analysis in this work is based on the theoretical Shannon capacity where both measured/experimental path loss and calibrated path loss models are taken into consideration. In this work, we assume a decode and forward scheme, a full-duplex relaying protocol and an optimized relay location is investigated. A 30 % of improvement in the macrocell capacity is achieved with the usage of relaying scenario compared to a classical macrocell network. Furthermore, increasing the relay antenna height from 4 meters to 12 meters can significantly increase the relay capacity to more than 20 % in suburban and moderate urban environments.</span>

scholarly journals Conditioning and PPP processing of smartphone GNSS measurements in realistic environments

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Ganga Shinghal ◽  
Sunil Bisnath
Keyword(s):  
Single Point ◽  
A Priori ◽  
Density Ratio ◽  
Satellite System ◽  
Quality Parameters ◽  
Urban Environments ◽  
Dual Frequency ◽  
Missing Measurements ◽  
Data Gaps ◽  
Point Positioning

AbstractSmartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (handheld, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments. In such environments, the system suffers from frequent phase loss-of-lock leading to data gaps. The smartphone measurements have low and irregular carrier-to-noise (C/N0) density ratio and high multipath, which leads to poor or no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine. Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS) error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were 20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic scenario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solution as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5–10% percentage point more availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged file cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution. The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Electronics Association) file was computed using point positioning with the aid of measurements from internal sensors. The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous computation of solution.

scholarly journals Gut Microbiota Dynamics in Natural Populations of Pintomyia evansi under Experimental Infection with Leishmania infantum

2021 ◽  
Vol 9 (6) ◽  
pp. 1214
Author(s):  
Rafael José Vivero ◽  
Victor Alfonso Castañeda-Monsalve ◽  
Luis Roberto Romero ◽  
Gregory D. Hurst ◽  
Gloria Cadavid-Restrepo ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Leishmania Infantum ◽  
Natural Populations ◽  
Illumina Miseq ◽  
Urban Environments ◽  
High Load ◽  
Leishmania Infection ◽  
Experimental Conditions ◽  
Rural And Urban ◽  
Negative Effect

Pintomyia evansi is recognized by its vectorial competence in the transmission of parasites that cause fatal visceral leishmaniasis in rural and urban environments of the Caribbean coast of Colombia. The effect on and the variation of the gut microbiota in female P. evansi infected with Leishmania infantum were evaluated under experimental conditions using 16S rRNA Illumina MiSeq sequencing. In the coinfection assay with L. infantum, 96.8% of the midgut microbial population was composed mainly of Proteobacteria (71.0%), followed by Cyanobacteria (20.4%), Actinobacteria (2.7%), and Firmicutes (2.7%). In insect controls (uninfected with L. infantum) that were treated or not with antibiotics, Ralstonia was reported to have high relative abundance (55.1–64.8%), in contrast to guts with a high load of infection from L. infantum (23.4–35.9%). ASVs that moderately increased in guts infected with Leishmania were Bacillus and Aeromonas. Kruskal–Wallis nonparametric variance statistical inference showed statistically significant intergroup differences in the guts of P. evansi infected and uninfected with L. infantum (p < 0.05), suggesting that some individuals of the microbiota could induce or restrict Leishmania infection. This assay also showed a negative effect of the antibiotic treatment and L. infantum infection on the gut microbiota diversity. Endosymbionts, such as Microsporidia infections (<2%), were more often associated with guts without Leishmania infection, whereas Arsenophonus was only found in guts with a high load of Leishmania infection and treated with antibiotics. Finally, this is the first report that showed the potential role of intestinal microbiota in natural populations of P. evansi in susceptibility to L. infantum infection.

Damage assessment mapping of rural environments; integration of SAR and Optical data

2021 ◽  
Author(s):  
Shiran Havivi ◽  
Stanley R. Rotman ◽  
Dan G. Blumberg ◽  
Shimrit Maman
Keyword(s):  
Natural Disaster ◽  
Rural Areas ◽  
Urban Areas ◽  
Damage Assessment ◽  
Vegetation Index ◽  
Urban Environments ◽  
Optical Data ◽  
Damage Level ◽  
Rural Environments ◽  
Rural And Urban

&lt;p&gt;The damage caused by a natural disaster in rural areas differs in nature, extent, landscape and in structure, from the damage in urban environments. Previous and current studies focus mainly on mapping damaged structures in urban areas after catastrophe events such as an earthquake or tsunami. Yet, research focusing on the damage level or its distribution in rural areas is absent. In order to apply an emergency response and for effective disaster management, it is necessary to understand and characterize the nature of the damage in each different environment.&amp;#160;&lt;/p&gt;&lt;p&gt;Havivi et al. (2018), published a damage assessment algorithm that makes use of SAR images combined with optical data, for rapid mapping and compiling a damage assessment map following a natural disaster. The affected areas are analyzed using interferometric SAR (InSAR) coherence. To overcome the loss of coherence caused by changes in vegetation, optical images are used to produce a mask by computing the Normalized Difference Vegetation Index (NDVI) and removing the vegetated area from the scene. Due to the differences in geomorphological settings and landuse\landcover between rural and urban settlements, the above algorithm is modified and adjusted by inserting the Modified Normalized Difference Water Index (MNDWI) to better suit rural environments and their unique response after a disaster. MNDWI is used for detection, identification and extraction of waterbodies (such as irrigation canals, streams, rivers, lakes, etc.), allowing their removal which causes lack of coherence at the post stage of the event. Furthermore, it is used as an indicator for highlighting prone regions that might be severely affected pre disaster event. Thresholds are determined for the co-event coherence map (&amp;#8804; 0.5), the NDVI (&amp;#8805; 0.4) and the MNDWI (&amp;#8805; 0), and the three layers are combined into one. Based on the combined map, a damage assessment map is generated.&amp;#160;&lt;/p&gt;&lt;p&gt;As a case study, this algorithm was applied to the areas affected by multi-hazard event, following the Sulawesi earthquake and subsequent tsunami in Palu, Indonesia, which occurred on September 28th, 2018. High-resolution COSMO-SkyMed images pre and post the event, alongside a Sentinel-2 image pre- event are used as inputs. The output damage assessment map provides a quantitative assessment and spatial distribution of the damage in both the rural and urban environments. The results highlight the applicability of the algorithm for a variety of disaster events and sensors. In addition, the results enhance the contribution of the water component to the analysis pre and post the event in rural areas. Thus, while in urban regions the spatial extent of the damage will occur in its proximity to the coastline or the fault, rural regions, even in significant distance will experience extensive damage due secondary hazards as liquefaction processes.&amp;#160; &amp;#160; &amp;#160;&lt;/p&gt;

scholarly journals Path loss models based on stochastic rays

2007 ◽  
Vol 1 (3) ◽  
pp. 602 ◽  
Author(s):  
L.Q. Hu ◽  
H. Yu ◽  
Y. Chen
Keyword(s):  
Path Loss ◽  
Loss Models
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