Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach

Thi-Tuyet-Hai Nguyen; Tran Cong-Hung; Nguyen Hong-Son; Tan Hanh; Tran Trung Duy; Lam-Thanh Tu

doi:10.4108/eetinis.v12i2.6780

Authors

Thi-Tuyet-Hai Nguyen Posts and Telecommunications Institute of Technology
Tran Cong-Hung Saigon International University
Nguyen Hong-Son Posts and Telecommunications Institute of Technology
Tan Hanh Posts and Telecommunications Institute of Technology
Tran Trung Duy Posts and Telecommunications Institute of Technology
Lam-Thanh Tu Ton Duc Thang University

DOI:

https://doi.org/10.4108/eetinis.v12i2.6780

Keywords:

Coverage probability, deep learning, energy harvesting, long range, power beacon

Abstract

The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory.

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