An enhanced modified multi wall propagation model

Hosseinzadeh, Salaheddin; Larijani, Hadi; Curtis, Krystyna

doi:10.1109/giots.2017.8016211

Cited by 15 publications

(12 citation statements)

References 12 publications

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“…While, in [20], a constant difference of 27 dB received signal power between Okumura-Hata and the LoRaWAN measurements was observed. An enhanced modified multi-wall propagation model and a neural network propagation model were presented by the authors in [21] and [22] respectively. Finally, a comparative performance analysis of Okumura-Hata, COST-231 Hata and COST-231 Walfish-Ikegami (COST-WI) propagation models was performed using the NS3 simulator and the measurements in an urban environment by Harinda et al [23].…”

Section: ) Itu-r 1225 Model: This Is a Radio Propagation Model Definmentioning

confidence: 99%

Performance Evaluation of Propagation Models for LoRaWAN in an Urban Environment

Ingabire

Larijani

Gibson

2020

2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)

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Low-Power, Wide-Area Networks (LPWAN) are projected to support a significant number of devices within the Internet of Things (IoT). Long-Range Wide Area Network (LoRaWAN) is an open specification and emerging LPWAN connectivity solution for IoT platforms. However, LoRaWAN network performance in urban scenarios is a fundamental research topic with limited exploration and characteristic analysis. In this paper, ATDI ICS Telecom is used to investigate LoRaWAN radio network coverage at 868 MHz using the Okumura-Hata, COST-231 Hata, Extended Hata, and ITU-R 1225 propagation models. The predicted received signal strength simulation results are compared with real-world test measurements taken in the urban environment of Glasgow City to evaluate various propagation models' accuracy. The proposed work demonstrates ITU R 225 and Extended Hata overestimated the real measured received signal strength power whereas, COST-231 Hata and Okumura-Hata underestimated the same signal power. Our results and analysis give important insights into the performance, evaluation, and comparison of existing propagation models for IoT connectivity with LoRaWAN technology within an urban environment.

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Section: ) Itu-r 1225 Model: This Is a Radio Propagation Model Definmentioning

confidence: 99%

Performance Evaluation of Propagation Models for LoRaWAN in an Urban Environment

Ingabire

Larijani

Gibson

2020

2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)

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“…The noise samples w l,n [k] are independent normal random variables with zero-mean and variance -70 dBm. Propagation adheres to the Motley-Keenan multi-wall radio propagation model [46], which accounts for the direct path, up to 5 firstorder wall reflections, and up to 5 wall-to-wall second-order reflections. Remarkably, the model captures the impact of the angle of incidence on the power of the reflected ray.…”

Section: Dealing With Missing Featuresmentioning

confidence: 99%

Location-Free Spectrum Cartography

Teganya

Romero

Lopez-Ramos

et al. 2019

IEEE Trans. Signal Process.

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Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.

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“…For instance, it is possible to consider two-and three-fold reflections/diffraction, and reflection/diffraction factors depend on the angle of incidence and permittivity of materials. Further detailed summaries of these models with their implementations are provided in [7][8][9][10]. Nevertheless, there are the following drawbacks to these deterministic models:…”

Section: Walfisch Ikegami and Ray Tracing Modelsmentioning

confidence: 99%

“…However, this approach can be prone to combinatorial explosion of rules, especially for complex systems. Considering that there was a total of seven inputs, each with two membership functions, then the total number of rules is 2 7 .…”

Section: Model Identificationmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

Hosseinzadeh

Larijani

Curtis

et al. 2019

ASI

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This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios.

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An enhanced modified multi wall propagation model

Cited by 15 publications

References 12 publications

Performance Evaluation of Propagation Models for LoRaWAN in an Urban Environment

Performance Evaluation of Propagation Models for LoRaWAN in an Urban Environment

Location-Free Spectrum Cartography

An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN

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