ML-Assisted RIS for ISAC Systems: Initial Results in the 6G Study Band
Phan, Duy Tung; Nguyen, Quoc Duy; Takanen, Niklas; Nguyen, Thanh Nhan; Juntti, Markku; Soh, Ping Jack (2025-05-21)
IEEE
21.05.2025
D. T. Phan, Q. D. Nguyen, N. Takanen, T. N. Nguyen, M. Juntti and P. J. Soh, "ML-Assisted RIS for ISAC Systems: Initial Results in the 6G Study Band," 2025 19th European Conference on Antennas and Propagation (EuCAP), Stockholm, Sweden, 2025, pp. 1-5, doi: 10.23919/EuCAP63536.2025.10999385
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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202506164496
https://urn.fi/URN:NBN:fi:oulu-202506164496
Tiivistelmä
Abstract
Integrated sensing and communication (ISAC) is essential for 6G networks to alleviate spectrum congestion while simultaneously cater to rising demands for sensing and communication. While reconfigurable intelligent surfaces (RIS) improve ISAC performance, accurate channel state information (CSI) remains a challenge. This paper proposes a machine learning (ML) approach to estimate the angle of arrival (AoA) in RIS-aided systems. By training an ML model using data collected in the 6G study band, RIS is observed to be capable of predicting AoA in different scenarios. For a limited number of scanning angles and at 6G study band from 6.0-7.5 GHz, prediction errors are below 1° with 91.7% scanning reduction. Additionally, by utilizing extra sensors to measure the distance from the receiver (Rx) to the origin and its x-coordinate, estimation errors are reduced to 0.8°. The results offer practical insights for balancing AoA estimation accuracy and system complexity of the ML-intergrated RIS for ISAC applications.
Integrated sensing and communication (ISAC) is essential for 6G networks to alleviate spectrum congestion while simultaneously cater to rising demands for sensing and communication. While reconfigurable intelligent surfaces (RIS) improve ISAC performance, accurate channel state information (CSI) remains a challenge. This paper proposes a machine learning (ML) approach to estimate the angle of arrival (AoA) in RIS-aided systems. By training an ML model using data collected in the 6G study band, RIS is observed to be capable of predicting AoA in different scenarios. For a limited number of scanning angles and at 6G study band from 6.0-7.5 GHz, prediction errors are below 1° with 91.7% scanning reduction. Additionally, by utilizing extra sensors to measure the distance from the receiver (Rx) to the origin and its x-coordinate, estimation errors are reduced to 0.8°. The results offer practical insights for balancing AoA estimation accuracy and system complexity of the ML-intergrated RIS for ISAC applications.
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