Classification of indoor environments based on spatial correlation of RF channel fingerprints

AlHajri, Mohamed I.; Alsindi, Nayef; Ali, Nazar; Shubair, Raed M.

doi:10.1109/aps.2016.7696430

Cited by 28 publications

(19 citation statements)

References 4 publications

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“…AI [69] is not part of the earlier generation networks (from 1G to 4G) [64]. It is part of the 5G network, officially from the latest addition in the 3rd Generation Partnership Project (3GPP) Release 18 specifications [35], which is expected to benefit the telecommunications sector with the development of various notable applications [70][71][72][73]. As part of the new 6G network, AI plays an essential role in revolutionizing communication and automation, such as in handover process [74][75][76][77][78], resources allocation [79,80], and network selection.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Alraih

Shayea

Behjati

et al. 2022

Sensors

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Ever since the introduction of fifth generation (5G) mobile communications, the mobile telecommunications industry has been debating whether 5G is an “evolution” or “revolution” from the previous legacy mobile networks, but now that 5G has been commercially available for the past few years, the research direction has recently shifted towards the upcoming generation of mobile communication system, known as the sixth generation (6G), which is expected to drastically provide significant and evolutionary, if not revolutionary, improvements in mobile networks. The promise of extremely high data rates (in terabits), artificial intelligence (AI), ultra-low latency, near-zero/low energy, and immense connected devices is expected to enhance the connectivity, sustainability, and trustworthiness and provide some new services, such as truly immersive “extended reality” (XR), high-fidelity mobile hologram, and a new generation of entertainment. Sixth generation and its vision are still under research and open for developers and researchers to establish and develop their directions to realize future 6G technology, which is expected to be ready as early as 2028. This paper reviews 6G mobile technology, including its vision, requirements, enabling technologies, and challenges. Meanwhile, a total of 11 communication technologies, including terahertz (THz) communication, visible light communication (VLC), multiple access, coding, cell-free massive multiple-input multiple-output (CF-mMIMO) zero-energy interface, intelligent reflecting surface (IRS), and infusion of AI/machine learning (ML) in wireless transmission techniques, are presented. Moreover, this paper compares 5G and 6G in terms of services, key technologies, and enabling communications techniques. Finally, it discusses the crucial future directions and technology developments in 6G.

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Alraih

Shayea

Behjati

et al. 2022

Sensors

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“…A methodology for the ML classification of indoor environments based on CTF and the frequency coherence function (FCF) was proposed by AlHajri et al [63]. They investigated how CTF and FCF vary within the room and they proved that FCF and CTF can be considered as a unique fingerprint of the environment.…”

Section: Indoor Environment Classificationmentioning

confidence: 99%

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

et al. 2021

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Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments.

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“…According to hierarchical clustering, they offered a natural abstraction for ensemble task encoding and control with regards to accurate yet flexible organizational features at different resolutions. In many papers a realistic indoor multi path environment classification developed for instance the approach used in [97] was according to statistics of correlation which were related to system bandwidth and grid spacing that illustrates a certain pattern that differentiates LOS open space and LOS cluttered. The outcomes are essential for creating a framework for developing a database of RF channel fingerprints for indoor multi path environment classification.…”

Section: Ins Problemsmentioning

confidence: 99%

“…Reduce the accumulated error, improve the generation ability and minimize the use of infrastructure with fuzzy decision tree (FDT) [95] KNN Improve accuracy, adaptation and provide continuous position information based on k-nearest neighbor algorithm (KNN) [100] SVM Increase the accuracy and decrease the costs, robust against noisy and large data set with a hybrid of the k-nearest neighbors algorithm and the Multi-Class Support Vector Machines (KNN-SVM) model [99] Coarse-grained turn estimation can be performed with very high accuracy [101] Clustering Offers a formalism for identifying with use of hierarchical effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of the hierarchical clustering method [96] k-means K-Means clustering is proposed to automatically identify and discard transient high amplitude interferences and make noise covariances estimation [102] Classification A realistic indoor multi path environment classification based on practical RF measurements that is a compromise between accuracy and resources/complexity [97] Regression Improve robustness and accuracy, localization error and the computation complexity based on regression tree Improvement in the positional accuracy base on Artificial Neural Network (ANN) [98] Support Vector Machine Regression (SVR) and Partial Least Squares Regression (PLSR) [103] Bayesian networks…”

Section: Decision Treementioning

confidence: 99%

Indoor navigation systems based on data mining techniques in internet of things: a survey

et al. 2018

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Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges.

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Classification of indoor environments based on spatial correlation of RF channel fingerprints

Cited by 28 publications

References 4 publications

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

Indoor navigation systems based on data mining techniques in internet of things: a survey

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