Optimization of Leaky Feeder Slot Spacing for Better Beam Forming in Mines and Tunnels

Hassan, Noha; Farjow, Wisam; Fernando, Xavier

doi:10.4236/ijcns.2016.94007

Cited by 4 publications

(2 citation statements)

References 20 publications

(20 reference statements)

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“…Therefore, the mining industry is actively pursuing developing various solutions to track miners in underground mines [144]. Although zone based RFID localization [145] is prevalent in mines, several new technologies including directional antennas [146], beam forming leaky feeders [147] and ML algorithms [112] are researched to improve the reliability and accuracy.…”

Section: Contextual-aware Location Based Marketingmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

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Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

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Section: Contextual-aware Location Based Marketingmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

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“…The LF system is typically less noisy and has enough bandwidth to support multiple radio frequency (RF) signals. The slot spacing, shape, and size determines the loss, radiation patterns, and coupling properties [8].…”

Section: Introductionmentioning

confidence: 99%

The Leaky Feeder, a Reliable Medium for Vehicle to Infrastructure Communications

Farahneh

Fernando

2019

ASI

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Reliable vehicular communications is fast becoming a necessity. Vehicle to infrastructure (V2I) communication, which is critical for safety, is often interrupted when vehicles travel in tunnels. Leaky Feeder (LF) or radiating cable have been the primary solution to provide wireless access in tunnels and mines, but being overlooked until now. The LF is a natural multi antenna transceiver ideal for broadband short rage access. In this work, we model the LF as a linear antenna array and derive the average bit error rate (BER) in Rayleigh fading channel considering Quadrature Phase Shift Keying (QPSK) and M-Array Quadrature Amplitude (M-QAM) Modulations. We consider maximal ratio transmission (MRT) at the transmission end and coherent detection and maximal ratio combining (MRC) at the receiving end. Analytical expressions are derived for the BER. The effects of slot spacing and carrier frequency on the BER are also studied. Numerical evaluations show that the LF is a strong candidate for tunnels with much lower BER than a single antenna transmitter with the same SNR.

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Reliable Communication Network for Emergency Response and Disaster Management in Underground Mines

Kamruzzaman

Fernando

Jaseemuddin

et al. 2018

Smart Technologies for Emergency Response and Disaster Management

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Emergency response and disaster management in underground mines are very challenging due to the hostile nature. Environment monitoring in mines has been an obligatory requirement to ensure safe working conditions for miners. Reliable communication network is essential to quickly detect the underground condition especially in emergency situation and to conduct proper rescue operations. This chapter presents an overview of reliable communication network needed for emergency response and disaster management in underground mines. The chapter begins by introducing the most common accidents occurring in the mining, underground mine environment and channel properties. Subsequently, communications in underground mines, existing underground communication and tracking systems, and disaster forecasting & mine safety management are discussed. The chapter also covers post-disaster mine communications & tracking systems and optimized backbone networks for underground mines. Finally, the chapter concludes by reporting relevant research at Ryerson Communications Lab and pointing out some open issues and possible research directions.

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Optimization of Leaky Feeder Slot Spacing for Better Beam Forming in Mines and Tunnels

Cited by 4 publications

References 20 publications

A Survey of Machine Learning for Indoor Positioning

A Survey of Machine Learning for Indoor Positioning

The Leaky Feeder, a Reliable Medium for Vehicle to Infrastructure Communications

Reliable Communication Network for Emergency Response and Disaster Management in Underground Mines

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