LoRa Channel Propagation Modelling using Artificial Neural Network

Rofi, Ahmad Shahmi Mod; Habaebi, Mohamed Hadi; Islam, Md. Rafiqul; Basahel, Ahmed

doi:10.1109/iccce50029.2021.9467234

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…The Okumura-Hata model considers path losses due to shadowing effects such as diffraction, reflection, and scattering of the signal [74]. It is applicable only for the frequency range between 150-1500 MHz and distances between 1-20 km.…”

Section: Okumura-hatamentioning

confidence: 99%

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On the Simulation of LoRaWAN Networks: A Focus on Reproducible Parameter Configuration

De Campos,

De Almeida,

Matheus

et al. 2024

Comput. Networks Commun.

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In the past decade, with the rapid proliferation of Internet of Things (IoT) devices new applications have emerged in diverse fields such as agriculture, smart cities, and healthcare. In this context, LoRaWAN, a Low-Power Wide Area Network protocol, has gained significant adoption. Its advantages, including low-power consumption and long-range communication capabilities, make it an ideal solution for IoT communication. Simulation tools have played a crucial role in facilitating experimentation and advancing the implementation of the LoRaWAN protocol. However, effectively modelling these experiments present a significant challenge. Through a comprehensive survey of the literature on LoRaWAN, we have identified variations in parameter configuration among different works and observed a lack of sufficient information in most of them, thereby impeding result reproducibility. This paper offers a comprehensive overview of LoRaWAN technology, encompassing its architecture and underlying technologies such as LoRa and LPWAN. Furthermore, we present and compare the main simulation tools currently available for LoRaWAN, discuss the parameters that significantly impact the performance of a LoRaWAN network, and explore how researchers have configured these parameters to model their simulation experiments.

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Section: Okumura-hatamentioning

confidence: 99%

“…According to [74], real experiments shown that the path losses for suburban (PLsuburban) and rural (PLrural) environments, can be predicted based on the respective equations:…”

Section:  mentioning

confidence: 99%

On the Simulation of LoRaWAN Networks: A Focus on Reproducible Parameter Configuration

De Campos,

De Almeida,

Matheus

et al. 2024

Comput. Networks Commun.

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“…Moreover, ref. [ 23 ] used LoRa technology to measure RSSI for a downlink scenario at 868 MHz in a tropical environment and utilized an ANN to model path loss.…”

Section: Related Workmentioning

confidence: 99%

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements

Cardoso

Barros

Carvalho

et al. 2022

Sensors

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The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322–1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714–1.3891 dB, with an error SD less than 1.1706 dB.

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“…For data collection, the receiver terminal must be placed away from its base station. The separation link between both the receiver and the transmitter terminal was measured over every meter starting at 10 meters up to 1 km [8].…”

Section: Measurementmentioning

confidence: 99%

ANN-based LoRaWAN Channel Propagation Model

Habaebi

Rofi

Islam

et al. 2022

Int. J. Interact. Mob. Technol.

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LoRaWAN wireless communication channels are often impacted by noise and interference over long-range causing loss of a received signal. One of the main drawbacks of using existing propagation models is less accurate as these models in designing the communication link are tailored to simplify the estimation. In this paper, an artificial intelligent real time path loss model is proposed. It is capable of processing complex variables over a short period of time. Providing it with enough data, the model is able to learn channel behavior and predict the path loss accurately. Results of the model are benchmarked against classical statistical curve fitting models where RMSE values are also compared and indicating that the artificial intelligent model has better accurate prediction.

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LoRa Channel Propagation Modelling using Artificial Neural Network

Cited by 4 publications

References 4 publications

On the Simulation of LoRaWAN Networks: A Focus on Reproducible Parameter Configuration

On the Simulation of LoRaWAN Networks: A Focus on Reproducible Parameter Configuration

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements

ANN-based LoRaWAN Channel Propagation Model

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