Lora channel propagation modelling using artificial neural network

Mod Rofi, Ahmad Shahmi and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul and Basahel, Ahmed (2021) Lora channel propagation modelling using artificial neural network. In: 2021 8th International Conference on Computer and Communication Engineering (ICCCE), 22-23 June 2021, Kuala Lumpur, Malaysia.

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Abstract

In Long Range (LoRa) wireless communication, the transmitted signals may experience loss due to many obstacles as well as interferences. The existing path loss propagation models which used for applications as the one used for LoRa networks are designed to make it simple for calculation and thus making it less accurate. Enabling technology such as artificial intelligence is capable to process complex variables in a very short duration with almost accurate prediction. By having enough data or collecting more data over certain period, this technology can be implemented to learn all the data rules and behavior to predict the output. In this paper, the Artificial Neural network model is proposed to predict the propagation loss of LoRa communication link. Results are compared against path loss propagation models and RMSE values are also determined. The proposed model shows improvement compared with other models in terms of Received Signal Strength Indicator (RSSI) performance and RMSE values.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	6727/90600
Uncontrolled Keywords:	Propagation loss, received power, neural network, artificially intelligent, LoRa.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	21 Jul 2021 12:57
Last Modified:	21 Jul 2021 12:57
URI:	http://irep.iium.edu.my/id/eprint/90600

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