Academic Journal
On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge
| العنوان: | On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge |
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| المؤلفون: | Chen, Chin-Hung, Karanov, Boris, van Houtum, Wim J., Wu, Yan, Young, Alex, Alvarado, Alex |
| المصدر: | Chen , C-H , Karanov , B , van Houtum , W J , Wu , Y , Young , A & Alvarado , A 2024 , On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge . in 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 . , 10570860 , Institute of Electrical and Electronics Engineers , 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 , Dubai , United Arab Emirates , 21/04/24 . https://doi.org/10.1109/WCNC57260.2024.10570860 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | BCJR algorithm, intersymbol interference, machine learning, neural networks, symbol detection |
| الوصف: | Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstrated resilience against inaccurate channel tap estimations when applied to a time-invariant channel with ideal exponential decay profiles. However, its generalization capabilities for practically-relevant time-varying channels, where the receiver can only access incorrect channel parameters, remain largely unexplored. The primary contribution of this paper is to expand upon the results from existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. Our findings demonstrate that BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios when learning from noisy channel data and with imperfect channel decay profiles. However, this advantage is shown to diminish when the operating channel is also rapidly time-varying. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet. An underestimation of the memory largely degrades the performance of both BCJR and BCJRNet, especially in a slow-decaying channel. To mimic a situation closer to a practical scenario, we also combined channel tap uncertainty with imperfect channel memory knowledge. Somewhat surprisingly, our results revealed improved performance when employing the conventional BCJR with an underestimated memory assumption. BCJRNet, on the other hand, showed a consistent performance improvement as the level of accurate memory knowledge increased. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.1109/WCNC57260.2024.10570860 |
| الاتاحة: | https://research.tue.nl/en/publications/0a549cce-24b5-4f8d-a9f3-c0daa5ad1695 https://doi.org/10.1109/WCNC57260.2024.10570860 https://pure.tue.nl/ws/files/337966761/WCNC2024_Chinhung_Final_ACK.pdf https://pure.tue.nl/ws/files/337967098/On_the_Robustness_of_Deep_Learning-Aided_Symbol_Detectors_to_Varying_Conditions_and_Imperfect_Channel_Knowledge.pdf http://www.scopus.com/inward/record.url?scp=85195279532&partnerID=8YFLogxK |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.CE991B07 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/WCNC57260.2024.10570860 |
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