scholarly journals An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

2021 ◽  
Author(s):  
Stefanos Sotirios Bakirtzis ◽  
Jiming Chen ◽  
Kehai Qiu ◽  
Jie Zhang ◽  
Ian Wassell
Keyword(s):  
Measurement Data ◽  
Wireless Channel ◽  
Propagation Model ◽  
Radio Propagation ◽  
Data Driven ◽  
Indoor Environments ◽  
Real World Data ◽  
Propagation Models ◽  
Frequency Bands ◽  
Propagation Modelling

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>

scholarly journals An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

2021 ◽  
Author(s):  
Stefanos Sotirios Bakirtzis ◽  
Jiming Chen ◽  
Kehai Qiu ◽  
Jie Zhang ◽  
Ian Wassell
Keyword(s):  
Measurement Data ◽  
Wireless Channel ◽  
Propagation Model ◽  
Radio Propagation ◽  
Data Driven ◽  
Indoor Environments ◽  
Real World Data ◽  
Propagation Models ◽  
Frequency Bands ◽  
Propagation Modelling

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>

scholarly journals INVESTIGATION ON NEED FOR SPECIFIC PROPAGATION MODEL FOR SPECIFIC ENVIRONMENT BASED ON DIFFERENT TERRAIN CHARACTERISTICS

2018 ◽  
Vol 19 (2) ◽  
pp. 90-104
Author(s):  
Jide Julius Popoola ◽  
Akinlolu Adediran Ponnle ◽  
Yekeen Olajide Olasoji ◽  
Samson Adenle Oyetunji
Keyword(s):  
Performance Evaluation ◽  
Field Strength ◽  
Measurement Data ◽  
Propagation Model ◽  
Propagation Loss ◽  
Detailed Knowledge ◽  
Propagation Models ◽  
Radio Stations ◽  
Service Environments ◽  
Terrain Characteristics

ABSTRACT: Owing to their speed of excution as well as their limited reliance on detailed knowledge of the terrain characteristics of the service environments, empirical propagation models have enjoyed general acceptability in the wireless communication research community. However, recent industrial observations show that no single propagation model can best fit all the radio service environments, which led to the hypothesis of specific models for specific environments. In order to scientifically verify this hypothesis, the study presented in this paper investigated the performance of the free space propagation loss (FSPL) model in two different radio environments characterised with different types of obstructions. The investigation was conducted through field strength distribution measurement of two broadcasting radio stations transmitting at 96.5 MHz and 102.3 MHz. The field strength measurement data obtained were analysed. The result of the analysis shows gross disparity between the measured path losses and calculated path losses using FSPL model. The disparity thus necessitates the modification of the FSPL model in order to develop each propagation model for each of the two radio stations employed and their environment. The developed models were then evaluated to ascertain their performances relative to the FSPL model. The performance evaluation results show that the predictions of the developed propagation models vary for each of the two environments. Furthermore, the comparative performance evaluation result of the developed models with similar studies in the literature shows that the developed models perform favourably. The overall result from the developed models confirms the hypothesis that each location requires a specific propagation model for proper radio wave design and quality of signal transmission and reception. ABSTRAK: Kelebihan yang ada pada kelajuan perlaksanaannya dan juga kurang pergantungannya pada butiran terperinci ciri-ciri khusus bentuk rupa bumi di persekitaran servisnya, model penyebaran empirik telah diterima umum dalam komuniti kajian komunikasi tanpa wayar. Walau bagaimanapun, pemerhatian industri terkini menunjukkan tidak ada sebarang model penyebaran yang sesuai bagi semua keadaan servis radio, ini menghala kepada hipotesis keperluan model tertentu pada keadaan servis tertentu. Bagi menentusahkan secara saintifik hipotesis ini, kajian yang dibentangkan dalam kertas ini mengkaji tentang prestasi model kehilangan penyebaran pada ruang bebas (FSPL) dalam dua persekitaran radio berlainan melalui beberapa jenis halangan berbeza. Kajian telah dijalankan ke atas dua stesen radio penyiaran pada frekuensi 96.5 MHz dan 102.3 MHz melalui ukuran sebaran ruang keupayaan. Data ukuran ruang keupayaan telah diperoleh dan dianalisa. Keputusan analisis menunjukkan keputusan tidak seragam yang melampau antara ukuran kehilangan laluan dan pada kiraan model FSPL. Ketidaksamaan ini memungkinkan keperluan mengubah model FSPL bagi membangunkan model penyebaran pada setiap dua radio stesen yang digunakan dan persekitarannya. Model yang dibangunkan ini kemudiannya dinilai bagi mengesahkan prestasinya dengan model FSPL. Keputusan penilaian menunjukkan perbezaan pada jangkaan model penyebaran bagi setiap dua keadaan. Tambahan, keputusan perbandingan model yang dibangunkan dalam karya ini adalah serupa seperti kajian lain yang berkaitan. Secara keseluruhannya model yang dibangunkan ini mengesahkan hipotesis bahawa setiap lokasi memerlukan model penyebaran bagi rekaan gelombang radio yang sesuai dan juga kualiti signal penyebaran dan penerimaan.

scholarly journals A Radio Propagation Model for Mixed Paths in Amazon Environments for the UHF Band

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
L. E. C. Eras ◽  
Diego K. N. da Silva ◽  
Fabrício B. Barros ◽  
Luís M. Correia ◽  
G. P. S. Cavalcante
Keyword(s):  
Electric Fields ◽  
Incidence Angle ◽  
Measurement Data ◽  
Propagation Model ◽  
Radio Propagation ◽  
Digital Television ◽  
Field Calculation ◽  
Radio Propagation Model ◽  
City Water ◽  
Uhf Band

This paper presents a radio propagation model for the UHF band that is designed for an outdoor scenario in the Amazon region of Brazil and comprises city, water, and forest environments. The model is designed for the Mobile and Home Digital Television (M-DTV and H-DTV) services. In the case of M-DTV, the electric field is calculated at user height, while for H-DTV it considers a fixed antenna on houses roofs. The field calculation is based on Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD). The results for M-DTV show a good agreement with measurement data in an Amazonian city (Belém) for 521 MHz. Different parameters of the proposed model are analyzed: the transition zone city-water, the level of water, the incidence angle in the forest, and electrical parameters for forest. Finally, the comparison that was made between the electric fields for H-DTV and M-DTV shows a difference of up to 19 dB.

scholarly journals An Optimized Propagation Model based on Measurement Data for Indoor Environments

2018 ◽  
Vol 2 ◽  
pp. 69-75 ◽  
Author(s):  
Marco Morocho-Yaguana ◽  
Patricia Ludeña-González ◽  
Francisco Sandoval ◽  
Betty Poma-Vélez ◽  
Alexandra Erreyes-Dota
Keyword(s):  
Measurement Data ◽  
Propagation Model ◽  
Average Error ◽  
Indoor Environments ◽  
University Campus ◽  
Campus Environment ◽  
Loss Parameter ◽  
Indoor Scenarios ◽  
Wireless Communications Systems ◽  
Parameter Values

Propagation is an essential factor ensuring good coverage of wireless communications systems. Propagation models are used to predict losses in the path between transmitter and receiver nodes. They are usually defined for general conditions. Therefore, their results are not always adapted to the behavior of real signals in a specific environment. The main goal of this work is to propose a new model adjusting the loss coefficients based on empirical data, which can be applied in an indoor university campus environment. The Oneslope, Log-distance and ITU models are described to provide a mathematical base. An extensive measurement campaign is performed based on a strict methodology considering different cases in typical indoor scenarios. New loss parameter values are defined to adjust the mathematical model to the behavior of real signals in the campus environment. The experimental results show that the model proposed offers an attenuation average error of 2.5% with respect to the losses measured. In addition, comparison of the proposed model with existing solutions shows that it decreases the average error significantly for all scenarios under evaluation.

scholarly journals Wi-Fi Fingerprinting Based Room Level Indoor Localization Framework Using Ensemble Classifiers

2019 ◽  
Vol 38 (1) ◽  
pp. 151-174 ◽  
Author(s):  
Beenish Ayesha Akram ◽  
Ali Hammad Akbar
Keyword(s):  
Neural Network ◽  
Real World ◽  
Indoor Localization ◽  
Propagation Model ◽  
Radio Propagation ◽  
Ensemble Classifiers ◽  
Propagation Models ◽  
Nearest Neighbours ◽  
Set Up ◽  
Realistic Information

Over the past decennium, Wi-Fi fingerprinting based indoor localization has seized substantial attention. Room level indoor localization can enable numerous applications to increase their diversity by incorporating user location. Real-world commercial scale deployments have not been realized because of difficulty in capturing radio propagation models. In case of fingerprinting based approaches, radio propagation model is implicitly integrated in the gathered fingerprints providing more realistic information as compared to empirical propagation models. We propose ensemble classifiers based indoor localization using Wi-Fi fingerprints for room level prediction. The major advantages of the proposed framework are, ease of training, ease to set up framework providing high room-level accuracy with trifling response time making it viable and appropriate for real time applications. It performs well in comparison with recurrently used ANN (Artificial Neural Network) and kNN (k-Nearest Neighbours) based solutions. Experiments performed showed that on our real-world Wi-Fi fingerprint dataset, our proposed approach achieved 89% accuracy whereas neural network and kNN based best found configurations achieved 85 and 82% accuracy respectively.

scholarly journals PATH LOSS MODELING FOR URBAN WIRLESS NETWORKS IN BAGHDAD

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-7-1-12
Author(s):  
Baseem G. Nsaif ◽  
◽  
Adheed H. Sallomi ◽  
Keyword(s):  
Wave Propagation ◽  
Radio Wave ◽  
Cellular Network ◽  
Measurement Data ◽  
Path Loss ◽  
Wireless Channel ◽  
Propagation Model ◽  
Radio Wave Propagation ◽  
Coverage Area ◽  
Wave Propagation Model

An accurate propagation modeling of radio waves propagation is very important task in cellular network design as it provides the detailed useful knowledge about the wireless channel environment characteristics. Theoretical or empirical RF propagation models provide the required useful information about the signal path loss and fading to evaluate the received signal level, the coverage area, and the outage probability in specific regions. This paper aimed to develop an empirical radio wave propagation model based on observations and sets of measurement data collected from different sites through drive test. These measurements are used to determine the received signal power at some locations to create an empirical radio wave propagation model that is suitable to be appropriate in cellular network accurate design and link budget prediction at the city of Baghdad.

Survey of Radio Propagation Models for Acoustic Applications in Underwater Wireless Sensor Network

2019 ◽  
Vol 09 ◽  
Author(s):  
Preeti Saini ◽  
Rishi Pal Singh ◽  
Adwitiya Sinha
Keyword(s):  
Acoustic Waves ◽  
Path Loss ◽  
Signal Strength ◽  
Propagation Model ◽  
Propagation Loss ◽  
Radio Propagation ◽  
Propagation Models ◽  
Loss Model ◽  
Path Loss Model ◽  
Radio Propagation Model

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.

scholarly journals Analysis of the selection of propagation models from outside into the building at 1800 MHz and 2100 MHz

SinkrOn ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 239-250
Author(s):  
Panangian Mahadi Sihombing ◽  
Maksum Pinem ◽  
Sri Indah Rezkika
Keyword(s):  
Mobile Communication ◽  
Measurement Data ◽  
Propagation Model ◽  
Wireless Internet ◽  
Propagation Models ◽  
Internet Service ◽  
Communication Devices ◽  
Service Coverage ◽  
Multiple Obstacles ◽  
Selection Of

Wireless internet service in educational buildings plays a crucial role in telecommunications for the knowledge sharing process. Therefore, various factors that may limit internet services coverage in the building should be eliminated or reduced. One such factor is path losses. Path losses are caused by multiple obstacles between the transmitting and receiving antennas. The problem of path losses in the education building can be solved by providing signal booster devices or Wireless Fidelity (Wi-Fi). But not all college buildings have such tools. Besides, WiFi devices also have limitations on bandwidth and the number of users. Thus, mobile communication devices or smartphones located inside the education buildings still need internet service coverage from the transmitter antenna outside the building. An accurate propagation model is required so that the transmitter antenna outside the building can provide internet service coverage to the inside of the building. This paper had been analyzed the selection of propagation models using three validation formulas, namely Mean Error (ME), Root Mean Square Error (RMSE), and Standard Deviation Error (SDE). This paper used several propagation models, namely the 3GPP Model, Winner+ Model, and COST231 Model. Based on the analysis of calculation and measurement data, it is known that the COST231 model is the most accurate because it has the lowest ME, RMSE, and SDE values.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

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