Learning Latent Wireless Dynamics From Channel State Information
Chaaya, Charbel Bou; Girgis, Abanoub M.; Bennis, Mehdi (2024-12-04)
IEEE
04.12.2024
C. Bou Chaaya, A. M. Girgis and M. Bennis, "Learning Latent Wireless Dynamics From Channel State Information," in IEEE Wireless Communications Letters, vol. 14, no. 2, pp. 489-493, Feb. 2025, doi: 10.1109/LWC.2024.3510943
https://rightsstatements.org/vocab/InC/1.0/
© 2024 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.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 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.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412177375
https://urn.fi/URN:NBN:fi:oulu-202412177375
Tiivistelmä
Abstract
In this work, we propose a novel data-driven ml technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations of high-dimensional csi, we incorporate a predictive component to capture the dynamics of the wireless system. Hence, we jointly learn a channel encoder that maps the estimated csi to an appropriate latent space, and a predictor that models the relationships between such representations. Accordingly, our problem boils down to training a jepa that simulates the latent dynamics of a wireless network from csi. We present numerical evaluations on measured data and show that the proposed jepa displays a two-fold increase in accuracy over benchmarks, for longer look-ahead prediction tasks.
In this work, we propose a novel data-driven ml technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations of high-dimensional csi, we incorporate a predictive component to capture the dynamics of the wireless system. Hence, we jointly learn a channel encoder that maps the estimated csi to an appropriate latent space, and a predictor that models the relationships between such representations. Accordingly, our problem boils down to training a jepa that simulates the latent dynamics of a wireless network from csi. We present numerical evaluations on measured data and show that the proposed jepa displays a two-fold increase in accuracy over benchmarks, for longer look-ahead prediction tasks.
Kokoelmat
- Avoin saatavuus [38841]