Localization-Based Radio Model Calibration for Fault-Tolerant Wireless Mesh Networks

Ivanov, S.; Nett, Edgar

doi:10.1109/tii.2012.2203311

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…For instance, at b ij = 0.01, the outage floor of 2.6 × 10 −8 is reached at about γ u = 20 dB, while at b ij = 0.1, the floor of 10 −5 is reached atγ u = 15 dB. In the asymptotic average internode SNR regime with fixed average uplink SNR, one can notice that The value of the outage floor can be obtained using (16), as…”

Section: Numerical Resultsmentioning

confidence: 99%

“…By inserting (16) and (19) into (14), the expression for asymptotic OP without link blockage can therefore be written as…”

Section: Case I:γ I γ Umentioning

confidence: 99%

“…Apart from the fading phenomena, one should account for macroscopic changes in the propagation environment, like large objects moving to block line-of-sight paths, and other obstacles [14][15][16]. Within this type of environment network, nodes are subject to dynamic shadowing and outage events occur due to propagation phenomena, which are not only static but are also slower than short-term fading.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Outage performance of low-latency decode-and-forward cooperative wireless networks

Zdravković

Cvetković

Kansanen

et al. 2016

J Wireless Com Network

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In this paper, we study a noise-limited low-latency decode-and-forward cooperative network set in a dynamic environment prone to link blockage. We analyse outage probability (OP) performance considering a temporally correlated fading channel between cooperating nodes employing a threshold-based protocol for internode communication. Exact and asymptotic analytical expressions for OP over Rayleigh fading, when maximal-ratio combining is applied at the destination, are derived. Asymptotic results give a better insight into outage performance at high average signal-to-noise ratio (SNR). We analyse the OP dependence on the SNR and link blockage probability and correlation coefficient of the estimated and actual internode SNR. The results illustrate that the internode protocol improves outage performance in the whole range of average uplink SNR. Internode channel correlation has a significant effect on the OP in the range of low average internode SNR. With increasing correlation, the OP increases up to an order of magnitude. The results also show that link blockage has a dominating effect on OP performance. The finite probability of all internode links being blocked reduces network performance to a single-link scenario, regardless of using the threshold-based protocol.

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Section: Numerical Resultsmentioning

confidence: 99%

“…By inserting (16) and (19) into (14), the expression for asymptotic OP without link blockage can therefore be written as…”

Section: Case I:γ I γ Umentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Outage performance of low-latency decode-and-forward cooperative wireless networks

Zdravković

Cvetković

Kansanen

et al. 2016

J Wireless Com Network

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show abstract

“…The proliferation of mobile devices has fostered great interest in indoor locationbased services, such as indoor navigation, robot tracking in the factories, locating workers on construction sites, and activity recognition [1,2,3,4,5,6,7,8], all requiring accurately identifying locations of mobile devices indoors. The indoor environment poses a complex radio propagation channel, including multipath propagation, blockage, and shadow fading, and stimulates great research efforts on indoor localization theory and systems [9].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for indoor localization based on bimodal CSI data

Wang

Mao

2019

Applications of Machine Learning in Wireless Communications

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In this chapter, we incorporate deep learning for indoor localization based on channel state information with commodity 5GHz WiFi. We first introduce the state-of-the-art deep learning techniques including deep autoencoder network, convolutional neural network, and recurrent neural network. We then present a deep learning based algorithm to leverage bi-modal CSI data, i.e., average amplitudes and estimated angle of arrivals (AOA), in both offline and online stages of fingerprinting. The proposed scheme is validated with extensive experiments. Finally, we examine several open research problems for indoor localization based on deep learning techniques.

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“…Due to their small size and low cost as compared to traditional data logging systems, wireless sensor nodes can be deployed at high densities, allowing a researcher to observe phenomena of interest at high spatial and temporal resolutions. WSNs have been used in applications such as gas leak detection [1], electro-static discharge event detection [2], item localization [3], radio propagation model calibration [4], and E-health [5]. In practically all applications of WSNs, location information is a critical requirement.…”

Section: Introduction 11 Received-signal Strength-based Localizationmentioning

confidence: 99%

Geometric filter algorithms for device-free localization using received-signal strength in wireless sensor networks

Talampas¹

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I would not be at this stage of my academic life without the support of a number of people. I would like to express my gratitude to: The DOST-ERDT program and the University of the Philippines, for the financial support throughout my graduate studies in Singapore. The students and sta↵ at the Satellite Research Centre, for warmly receiving and accommodating me during my stay. It was a pleasure working in the company of such motivated and talented people. My supervisor, Dr. Kay-Soon Low, for giving me space to pursue my research interests, and for providing valuable feedback and firm guidance on my work. I am honored to have completed my Ph.D. under his supervision. My family-parents Cesar and Noemi, and siblings Yey, Kel, Ninay, and Yayus. I am thankful for their quiet encouragement, and for taking full advantage of every opportunity for us to connect while I was away. And lastly, my wife, Candice, for not letting distance get in the way of expressing her love and support, and for sharing in all the trials, tribulations, and triumphs that have culminated at this point.

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Localization-Based Radio Model Calibration for Fault-Tolerant Wireless Mesh Networks

Cited by 15 publications

References 21 publications

Outage performance of low-latency decode-and-forward cooperative wireless networks

Outage performance of low-latency decode-and-forward cooperative wireless networks

Deep learning for indoor localization based on bimodal CSI data

Geometric filter algorithms for device-free localization using received-signal strength in wireless sensor networks

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