CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach

Wang, Xuyu; Gao, Le; Mao, Shiwen

doi:10.1109/jiot.2016.2558659

Cited by 365 publications

(189 citation statements)

References 26 publications

Supporting

Mentioning

188

Contrasting

Unclassified

Order By: Relevance

“…Considering the drawbacks of adopting RSS measurement as fingerprints like its high randomness and loose correlation with propagation distance, authors in [127] propose to utilize the calibrated CSI phase information for indoor fingerprinting. Specifically, in the off-line phase, a deep autoencoder network is constructed for each position to reconstruct the collected calibrated CSI phase information, and the weights are recorded as the fingerprints.…”

Section: Self-organizing Mapmentioning

confidence: 99%

See 1 more Smart Citation

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

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

516

290

View full text Add to dashboard Cite

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.

show abstract

Section: Self-organizing Mapmentioning

confidence: 99%

“…Moreover, ideas similar to [127] are adopted in [128] and [129]. In [128], CSI amplitude responses are taken as the input of the deep NN, which is trained by a greedy learning algorithm layer by layer to reduce complexity.…”

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

Self Cite

516

290

View full text Add to dashboard Cite

show abstract

“…For instance, in the physical layer, network processes that can be benefited include spatial spectrum sensing for cognitive radio and interference coordination in 5G, slow adaptive modulation and coding or channel estimation, beamforming, pilot decontamination in MIMO systems as described in [264], [265], and Channel State Information (CSI) estimation [266]- [269]. Medium Access Control (MAC) layer applications include resource scheduling algorithms (e.g., for frequency reuse), inter-cell interference coordination techniques, and multicasting algorithms.…”

Section: Technology Roadmap and Industry Trendsmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

View full text Add to dashboard Cite

Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

show abstract

“…Furthermore, it has a remarkable ability in extracting useful features and then uses these features in different classification tasks. Although some recent works have shown promising results [162][163][164][165][166][167], some challenges still need more investigations. Further research must be conducted to propose deep learning architectures that best suits sensing systems.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Wi‐Fi sensing: applications and challenges

Khalili

Soliman

Asaduzzaman

et al. 2020

J. eng.

View full text Add to dashboard Cite

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted.

show abstract

CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach

Cited by 365 publications

References 26 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

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Wi‐Fi sensing: applications and challenges

Contact Info

Product

Resources

About