Unsupervised Charting of Wireless Channels

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

doi:10.1109/glocom.2018.8647535

Cited by 6 publications

(11 citation statements)

References 27 publications

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“…NN-based positioning from CSI measurements requires carefully-designed features, which are robust to small-scale fading as well as system and hardware impairments. The use of beamspace representations to extract the incident angles has been used in [21], [27], [28], [47]. The conversion of subcarrier CSI into the delay domain to extract relative time-of-flight information has been used in [21], [47]- [49].…”

Section: B Contributionsmentioning

confidence: 99%

“…The conversion of subcarrier CSI into the delay domain to extract relative time-of-flight information has been used in [21], [47]- [49]. The use of cross-correlation in the spatial domain and 1dimensional autocorrelation in the delay domain has been used in [27], [28] and [20], [29], [50], respectively. Such methods improve resilience to small-scale fading and common hardware impairments, including time synchronization errors as well as residual carrier frequency and sampling rate offsets.…”

Section: B Contributionsmentioning

confidence: 99%

“…In order to enable CSI-based positioning, it is critical to construct robust CSI-features that (i) are unique for a given UE location, (ii) are robust to small-scale fading effects [19], [22], [27], [28], [59], and (iii) are resilient to real-world system and hardware impairments [20], [29]. Clearly, for a given UE location x (u) , the CSI features extracted from the estimated CSI {H (u) w [b]} should be unique-otherwise, UE location is not uniquely determined.…”

Section: A Csi-feature Extractionmentioning

confidence: 99%

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CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

Gönültaş

Lei

Langerman

et al. 2022

IEEE Trans. Wireless Commun.

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Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions. In this paper, we propose a positioning pipeline for wireless LAN MIMO-OFDM systems which uses uplink CSI measurements obtained from one or more unsynchronized access points (APs). For each AP receiver, novel features are first extracted from the CSI that are robust to system impairments arising in real-world transceivers. These features are the inputs to a NN that extracts a probability map indicating the likelihood of a UE being at a given grid point. The NN output is then fused across multiple APs to provide a final position estimate. We provide experimental results with realworld indoor measurements under line-of-sight (LoS) and non-LoS propagation conditions for an 80 MHz bandwidth IEEE 802.11ac system using a two-antenna transmit UE and two AP receivers each with four antennas. Our approach is shown to achieve centimeter-level median distance error, an order of magnitude improvement over a conventional baseline.

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Section: B Contributionsmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

Section: A Csi-feature Extractionmentioning

confidence: 99%

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CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

Gönültaş

Lei

Langerman

et al. 2022

IEEE Trans. Wireless Commun.

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“…Representation constraints are important for positioning users in wireless systems using channel charting (CC) [9], [10]. CC measures high-dimensional channel-state information (CSI) of user equipments (UEs) transmitting data to an access point or cell tower.…”

Section: A Representation-constrained Autoencoders For Positioningmentioning

confidence: 99%

“…With the success of deep neural networks, AEs are also gaining increased attention for unsupervised learning tasks [5]. Notable application examples of AEs include learning word embeddings [6], image compression [7], generative models [8], and channel charting [9], [10]. AEs are typically trained in an unsupervised manner, i.e., no labels are used, while potential side information on the training data is routinely ignored or application-specific representation structure is not imposed during training.…”

Section: Introductionmentioning

confidence: 99%

Improving Channel Charting with Representation -Constrained Autoencoders

Huang

Castañeda

Gönültaş

et al. 2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.

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Channel Charting: Locating Users Within the Radio Environment Using Channel State Information

et al. 2018

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We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC works in a fully unsupervised manner, i.e., learning is only based on channel state information (CSI) that is passively collected at a single point in space, but from multiple transmit locations in the area over time. The method then extracts channel features that characterize large-scale fading properties of the wireless channel. Finally, the channel charts are generated with tools from dimensionality reduction, manifold learning, and deep neural networks. The network element performing CC may be, for example, a multi-antenna base-station in a cellular system and the charted area in the served cell. Logical relationships related to the position and movement of a transmitter, e.g., a user equipment (UE), in the cell can then be directly deduced from comparing measured radio channel characteristics to the channel chart. The unsupervised nature of CC enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, hand-over, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base-station, without the need of information from global navigation satellite systems.

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Unsupervised Charting of Wireless Channels

Cited by 6 publications

References 27 publications

CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

Improving Channel Charting with Representation -Constrained Autoencoders

Channel Charting: Locating Users Within the Radio Environment Using Channel State Information

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