Indoor Localization Without a Prior Map by Trajectory Learning From Crowdsourced Measurements

Yoo, Jaehyun; Johansson, Karl Henrik; Kim, Hyoun Jin

doi:10.1109/tim.2017.2729438

Cited by 33 publications

(25 citation statements)

References 35 publications

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“…Result shows that the size of the radio map can be reduced by 72% with 2m localization error. In [126], PCA is also employed together with linear discriminant analysis to extract lower dimensional features from raw RSS measurement.…”

Section: Self-organizing Mapmentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

516

290

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As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

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Section: Self-organizing Mapmentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

516

290

View full text Add to dashboard Cite

show abstract

“…By subtracting the covariance matrix of the white noise samples from the covariance matrix of the signal samples and applying PCA into the remaining covariance matrix, the cognitive user can obtain the largest principal component of the remaining covariance matrix, which can be used as a good test statistic for spectrum sensing. In [63], the authors have developed a PCA-based radio localization scheme. In particular, the received signal strength (RSS) contains the information of user's location, which is a random variable due to multi-path impacts.…”

Section: B Learning From Radio Environmentmentioning

confidence: 99%

20 Years of Evolution From Cognitive to Intelligent Communications

Qin

Zhou

Zhang

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AIenabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.

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“…They derived the significant information by feature extraction. The feature representations have been categorized by a Gaussian Process (GP) regression algorithm [ 28 ]. Lee et al created an IPS where the basic service set identity (BSSID) has also been learned along with the RSS record to classify indoor locations.…”

Section: Associated Work On Ipsmentioning

confidence: 99%

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Rana

Prieto

Dey

et al. 2018

Sensors

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Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

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Indoor Localization Without a Prior Map by Trajectory Learning From Crowdsourced Measurements

Cited by 33 publications

References 35 publications

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

20 Years of Evolution From Cognitive to Intelligent Communications

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

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