Hybrid image/ray-shooting UHF radio propagation predictor for populated indoor environments

1999 ◽  
Vol 35 (21) ◽  
pp. 1804 ◽  
Author(s):  
F. Villanese ◽  
W.G. Scanlon ◽  
N.E. Evans ◽  
E. Gambi
Keyword(s):  
Radio Propagation ◽  
Indoor Environments ◽  
Ray Shooting

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>

AUTOMATIC GEOMETRY-DRIVEN OFDM QUALITY-OF-SERVICE ANALYSIS FOR INDOOR ENVIRONMENTS

2006 ◽  
Vol 07 (01) ◽  
pp. 147-161 ◽  
Author(s):  
HSIAO-CHUN WU ◽  
JI CHEN ◽  
ARJAN DURRESI ◽  
HUAIBEI ZHOU
Keyword(s):  
Quality Of Service ◽  
System Performance ◽  
Service Providers ◽  
Error Rates ◽  
Radio Propagation ◽  
Analysis Tool ◽  
Indoor Environments ◽  
Propagation Modeling ◽  
Channel Characterization

This paper introduces a novel geometry-based simulation technique for the arbitrary indoor OFDM quality-of-service (QoS) analysis. This automatic geometry-driven approach integrates electromagnetic radio propagation modeling, channel characterization, and OFDM interference analysis to quantify the indoor OFDM performance through the off-site computer–aided procedures. Essential OFDM quality-of-service measures, such as signal-to-interference ratios (SIR) and bit error rates (BER) can be generated by this new analysis tool. The resulting QoS measure contours can be used by service providers and OFDM communication system designers to quantify the system performance and determine the optimal accessing locations for any indoor geometry.

scholarly journals A novel 3D ray launching technique for radio propagation prediction in indoor environments

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0201905 ◽  
Author(s):  
Tan Kim Geok ◽  
Ferdous Hossain ◽  
Alan Tan Wee Chiat
Keyword(s):  
Radio Propagation ◽  
Indoor Environments ◽  
Propagation Prediction ◽  
Ray Launching
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