A Machine Learning Approach for the Classification of Indoor Environments Using RF Signatures

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

doi:10.1109/globalsip.2018.8646600

Cited by 20 publications

(19 citation statements)

References 9 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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“…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. In their later work, they explored several ML classification methods using different combinations of metrics as features [12,13]. Their findings are limited to the classification of four different environments with different levels of clutter: no clutter, low clutter, medium clutter, and high clutter.…”

Section: Indoor Environment Classificationmentioning

confidence: 99%

“…Their path is longer compared to the path of the direct signal, which is expressed as a time delay and additional attenuation of the reflected signals [11]. Thus, the set of reflected signals with their delays and amplitudes is characteristic for the particular environment and is known as the radio-frequency or RE signature [12]. The RE signature can be presented as the channel transform function (CTF) [13], but in order to exploit the full potential of multipath propagation, we apply a set of the strongest multipath components described by received power, delay, phase shift, and angle of arrival (AoA) as the RE signature.…”

Section: Introductionmentioning

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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“…The last three decades have witnessed an exponential growth and tremendous developments in wireless technologies and techniques, and their associated applications. These include indoor localization techniques and related aspects [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], terahertz communications and signal processing applications [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], and antenna design and propagation characteristics .…”

Section: Introductionmentioning

confidence: 99%

IRS for Multi-Access Edge Computing in 6G Networks

Mahbub¹,

Shubair²

2022

Preprint

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A Machine Learning Approach for the Classification of Indoor Environments Using RF Signatures

Cited by 20 publications

References 9 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

IRS for Multi-Access Edge Computing in 6G Networks

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