Channel Charting: Locating Users within the Radio Environment using Channel State Information

Studer, Christoph; Medjkouh, Said; Gönültaş, Emre; Goldstein, Tom; Tirkkonen, Olav

doi:10.48550/arxiv.1807.05247

Cited by 1 publication

(4 citation statements)

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“…Let us consider a scenario where two objects fall under the same channel tap and Doppler bin (i.e. Δ𝑣 < 𝜆 𝑁 𝑐 𝑇 𝑐 ) as shown in (11), they cannot be distinguished solely based on the Doppler domain channel, so we further explore the spatial dimension to make them differentiable. The objective of the conversion of the Doppler domain channel to angular domain, is to resolve the transmit and arrival paths of rays into angular bins.…”

Section: Channel In Delay-doppler-angular Domainmentioning

confidence: 99%

“…This information suggests if the separation of persons is more than 17 cm, they can be distinguished. Similarly, using (11), velocity resolution is 4 cm/sec where 𝜆 is 5mm as the carrier frequency is 60GHz, 𝑁 𝑐 is 128 and 𝑇 𝑐 is 1ms. Considering 16 Doppler domain bins, two persons with a velocity difference up to 64 cm/sec can be distinguished if they are at least 4 cm/sec apart in velocity.…”

Section: Experimental Setup and Perfor-mance Evaluationmentioning

confidence: 99%

“…As the RSSI-based method suffers in NLOS conditions, in [10], location-speci ic Channel State Information (CSI) is used as a ingerprint and is claimed to provide 5cm precision in an 20cm×70cm area in a non-line-of-sight of ice environment with one link measurement. In [11], a solution for Simultaneous Localization And Mapping (SLAM) is proposed using channel state information. It captures the local spatial geometry of the area using CSI in a channel chart in such a way that points which are closer in space also appears closer in the channel chart.…”

Section: Introductionmentioning

confidence: 99%

“…As shown in (11), velocity resolution depends on the number of the samples used for Doppler domain processing and the time duration of a packet.…”

mentioning

confidence: 99%

See 3 more Smart Citations

AI-based indoor localization using mmWave MIMO channel at 60 GHz

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Chavva²,

Agrawal³

2022

ITU J-FET

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In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)

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Section: Channel In Delay-doppler-angular Domainmentioning

confidence: 99%

Section: Experimental Setup and Perfor-mance Evaluationmentioning

confidence: 99%