A Tutorial on Terahertz-Band Localization for 6G Communication Systems

Chen, Hui; Sarieddeen, Hadi; Ballal, Tarig; Wymeersch, Henk; Alouini, Mohamed‐Slim; Al-Naffouri, Tareq Y.

doi:10.48550/arxiv.2110.08581

Cited by 7 publications

(14 citation statements)

References 169 publications

(294 reference statements)

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“…3) BSE: The steering vector in (4) is the same for all the subcarriers (frequency-independent). When the BSE is considered [4], [12], the FF steering vector a(ϑ) need to be modified to be frequency-dependent as ãk (ϑ), with each element ãk,n (ϑ) = e jπ 2n−N −1 2 sin(ϑ) λc λ k . This mismatch can be seen from λ k (instead of λ c ) in (8).…”

Section: True Channel Modelmentioning

confidence: 99%

“…In this work, these two vectors have a one-to-one mapping and either of them can sufficiently describe the channel model. Regardless of which vector is used, the FIM of the vector θ can be expressed as [4]…”

Section: B Crbmentioning

confidence: 99%

“…From the angle and delay estimation with respect to known anchors, e.g., base stations (BSs), the user equipment (UE) position can be estimated. Benefiting from the large bandwidth and array size of mmWave and THz systems, high angular and delay resolution, and hence accurate localization performance is expected [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

Chen¹,

Elzanaty²,

Ghazalian³

et al. 2022

Preprint

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Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large MIMO systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the MCRB (misspecified Cramér-Rao lower bound) to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.

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Section: True Channel Modelmentioning

confidence: 99%

Section: B Crbmentioning

confidence: 99%

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Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

Chen¹,

Elzanaty²,

Ghazalian³

et al. 2022

Preprint

Self Cite

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“…Due to the geometrical channel property of high frequency radio signals [3], the localization and mapping tasks can be done by exploiting the channel parameters (e.g., angles and delay) of each path from the estimated channel matrix [4]. With the help of one or more reference points (e.g., base station (BS) or reconfigurable intelligent surface (RIS)), the position and orientation of the user equipment (UE), as well as the position of the incidence point (IP), can be estimated [5].…”

Section: Introductionmentioning

confidence: 99%

Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization

Chen¹,

Fan²,

Yu³

et al. 2022

Preprint

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Radio localization is a key enabler for joint communication and sensing in the fifth/sixth generation (5G/6G) communication systems. With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array. However, the antenna array at the UE side increases the hardware and computational cost, preventing localization functionality. In this work, we show that with Doppler estimation and MPCs, localization and mapping tasks can be performed even with a singleantenna mobile UE. Furthermore, we show that the localization and mapping performance will improve and then saturate at a certain level with an increased UE speed. Both theoretical Cramér-Rao bound analysis and simulation results show the potential of localization under mobility and the effectiveness of the proposed localization algorithm.

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“…By properly configuring the element coefficients, RISs provide controllable communication channels to support communication and localization services for wireless devices [4]. With the properties of being energyefficient, lightweight, easy to deploy, and compatible with the existing wireless infrastructures, RISs are expected to be the vital enabler for the sixth generation (6G) communication systems [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Bi-Static Sensing for Near-Field RIS Localization

Ghazalian¹,

Keikhosravi²,

Chen³

et al. 2022

Preprint

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We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multicarrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramér-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.

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A Tutorial on Terahertz-Band Localization for 6G Communication Systems

Cited by 7 publications

References 169 publications

Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective

Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization

Bi-Static Sensing for Near-Field RIS Localization

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