Deep Learning-Based Smart Parking Solution using Channel State Information in LTE-Based Cellular Networks

Sonny, Amala and Rai, Prabhat Kumar and Kumar, Abhinav and Khan, Mohammed Zafar Ali (2020) Deep Learning-Based Smart Parking Solution using Channel State Information in LTE-Based Cellular Networks. In: 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 7 January 2020 - 11 January 2020.

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Abstract

The rapid increase in number of vehicles in recent times has adversely affected the travel time, traffic blocks, and accidents. Random search for a parking space contributes around 30% of city traffic which costs a significant amount of time and energy. Hence, smart parking solutions that detect and allocate vacant parking spaces in real-time are essential to minimize this traffic congestion. In this paper, we propose a novel method to detect the occupancy status of an outdoor parking space using Long Term Evolution (LTE)-based Channel State Information (CSI) and Convolutional Neural Network (CNN). This supervised classification method can provide real-time status of the occupancy. In this study, we analyze the performance of the proposed method by comparing with other CSI-based localization techniques. Through numerical results, we show that the proposed method outperforms the state-of-the-art techniques.

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IITH Creators:
IITH CreatorsORCiD
Sonny, AmalaUNSPECIFIED
Rai, Prabhat KumarUNSPECIFIED
Kumar, AbhinavUNSPECIFIED
Khan, Mohammed Zafar AliUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Subjects: Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 15 Jul 2021 07:54
Last Modified: 15 Jul 2021 07:54
URI: http://raiith.iith.ac.in/id/eprint/8340
Publisher URL: http://doi.org/10.1109/COMSNETS48256.2020.9027447
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