CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems

Jianzheng Li; Weijiang Wang; Rongkun Jiang; Shihan Huang

doi:10.1109/GCWkshps58843.2023.10464850

CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems

Jianzheng Li, Weijiang Wang, Rongkun Jiang^*, Shihan Huang

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Integrated sensing and communications (ISAC) is a promising technology for the sixth generation (6G) of mobile communications. High-precision positioning through sensing can significantly enhance subsequent communication for mobile users. This study proposes a method extracting spatial location information using estimated channel state information (CSI) to solve the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR- RIS)-assisted bilateral user localization problem. Existing neural network-based localization algorithms suffer from low accuracy and difficulty distinguishing users in similar areas. In contrast, this paper pioneeringly proposed the CsiNet-Former network, which achieves accurate user three-dimensional (3D) location estimation and trajectory prediction by extracting spatial feature information from CSI in a few time slots. This paper also compares the impact of the STAR-RIS in different modes on user localization accuracy. Simulation results show that the proposed method accurately locates and predicts the trajectory of bilateral users in the energy splitting (ES) mode using one set of network parameters under a suitable energy splitting ratio. CsiNet-Former achieves an average bilateral localization and prediction error of 0.75 m, with a single-side error of 0.034 m, outperforming existing location methods while covering a larger detectable area.

Original language	English
Title of host publication	2023 IEEE Globecom Workshops, GC Wkshps 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1886-1891
Number of pages	6
ISBN (Electronic)	9798350370218
DOIs	https://doi.org/10.1109/GCWkshps58843.2023.10464850
Publication status	Published - 2023
Event	2023 IEEE Globecom Workshops, GC Wkshps 2023 - Kuala Lumpur, Malaysia Duration: 4 Dec 2023 → 8 Dec 2023

Publication series

Name	2023 IEEE Globecom Workshops, GC Wkshps 2023

Conference

Conference	2023 IEEE Globecom Workshops, GC Wkshps 2023
Country/Territory	Malaysia
City	Kuala Lumpur
Period	4/12/23 → 8/12/23

Keywords

Bilateral User 3D Localization
MISO
Machine Learning
STAR-RIS
Trajectory Prediction

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10.1109/GCWkshps58843.2023.10464850

Cite this

@inproceedings{f862d896d1f646869ebdf1c74703e4bb,

title = "CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems",

abstract = "Integrated sensing and communications (ISAC) is a promising technology for the sixth generation (6G) of mobile communications. High-precision positioning through sensing can significantly enhance subsequent communication for mobile users. This study proposes a method extracting spatial location information using estimated channel state information (CSI) to solve the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR- RIS)-assisted bilateral user localization problem. Existing neural network-based localization algorithms suffer from low accuracy and difficulty distinguishing users in similar areas. In contrast, this paper pioneeringly proposed the CsiNet-Former network, which achieves accurate user three-dimensional (3D) location estimation and trajectory prediction by extracting spatial feature information from CSI in a few time slots. This paper also compares the impact of the STAR-RIS in different modes on user localization accuracy. Simulation results show that the proposed method accurately locates and predicts the trajectory of bilateral users in the energy splitting (ES) mode using one set of network parameters under a suitable energy splitting ratio. CsiNet-Former achieves an average bilateral localization and prediction error of 0.75 m, with a single-side error of 0.034 m, outperforming existing location methods while covering a larger detectable area.",

keywords = "Bilateral User 3D Localization, MISO, Machine Learning, STAR-RIS, Trajectory Prediction",

author = "Jianzheng Li and Weijiang Wang and Rongkun Jiang and Shihan Huang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Globecom Workshops, GC Wkshps 2023 ; Conference date: 04-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/GCWkshps58843.2023.10464850",

language = "English",

series = "2023 IEEE Globecom Workshops, GC Wkshps 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1886--1891",

booktitle = "2023 IEEE Globecom Workshops, GC Wkshps 2023",

address = "United States",

}

Li, J, Wang, W , Jiang, R & Huang, S 2023, CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems. in 2023 IEEE Globecom Workshops, GC Wkshps 2023. 2023 IEEE Globecom Workshops, GC Wkshps 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1886-1891, 2023 IEEE Globecom Workshops, GC Wkshps 2023, Kuala Lumpur, Malaysia, 4/12/23. https://doi.org/10.1109/GCWkshps58843.2023.10464850

CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems. / Li, Jianzheng; Wang, Weijiang ; Jiang, Rongkun et al.
2023 IEEE Globecom Workshops, GC Wkshps 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1886-1891 (2023 IEEE Globecom Workshops, GC Wkshps 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems

AU - Li, Jianzheng

AU - Wang, Weijiang

AU - Jiang, Rongkun

AU - Huang, Shihan

PY - 2023

Y1 - 2023

N2 - Integrated sensing and communications (ISAC) is a promising technology for the sixth generation (6G) of mobile communications. High-precision positioning through sensing can significantly enhance subsequent communication for mobile users. This study proposes a method extracting spatial location information using estimated channel state information (CSI) to solve the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR- RIS)-assisted bilateral user localization problem. Existing neural network-based localization algorithms suffer from low accuracy and difficulty distinguishing users in similar areas. In contrast, this paper pioneeringly proposed the CsiNet-Former network, which achieves accurate user three-dimensional (3D) location estimation and trajectory prediction by extracting spatial feature information from CSI in a few time slots. This paper also compares the impact of the STAR-RIS in different modes on user localization accuracy. Simulation results show that the proposed method accurately locates and predicts the trajectory of bilateral users in the energy splitting (ES) mode using one set of network parameters under a suitable energy splitting ratio. CsiNet-Former achieves an average bilateral localization and prediction error of 0.75 m, with a single-side error of 0.034 m, outperforming existing location methods while covering a larger detectable area.

AB - Integrated sensing and communications (ISAC) is a promising technology for the sixth generation (6G) of mobile communications. High-precision positioning through sensing can significantly enhance subsequent communication for mobile users. This study proposes a method extracting spatial location information using estimated channel state information (CSI) to solve the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR- RIS)-assisted bilateral user localization problem. Existing neural network-based localization algorithms suffer from low accuracy and difficulty distinguishing users in similar areas. In contrast, this paper pioneeringly proposed the CsiNet-Former network, which achieves accurate user three-dimensional (3D) location estimation and trajectory prediction by extracting spatial feature information from CSI in a few time slots. This paper also compares the impact of the STAR-RIS in different modes on user localization accuracy. Simulation results show that the proposed method accurately locates and predicts the trajectory of bilateral users in the energy splitting (ES) mode using one set of network parameters under a suitable energy splitting ratio. CsiNet-Former achieves an average bilateral localization and prediction error of 0.75 m, with a single-side error of 0.034 m, outperforming existing location methods while covering a larger detectable area.

KW - Bilateral User 3D Localization

KW - MISO

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DO - 10.1109/GCWkshps58843.2023.10464850

M3 - Conference contribution

AN - SCOPUS:85190270012

T3 - 2023 IEEE Globecom Workshops, GC Wkshps 2023

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EP - 1891

BT - 2023 IEEE Globecom Workshops, GC Wkshps 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Globecom Workshops, GC Wkshps 2023

Y2 - 4 December 2023 through 8 December 2023

ER -

CsiNet-Former Network for Bilateral User 3D Localization in STAR-RIS-Assisted MISO Systems

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Keywords

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