A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection
36th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2025, İstanbul, Türkiye, 1 - 04 Eylül 2025, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/pimrc62392.2025.11274560
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Convolutional Neural Network (CNN), Faster-than-Nyquist (FTN), Intersymbol Interference (ISI) Mitigation, Low-Complexity Detection, Structured Fixed Kernel CNN
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier layers for strong ISI patterns and fewer to later layers for weaker ones. This design improves detection accuracy while reducing redundant operations. Simulation results show that the detector achieves near-optimal bit error rate (BER) performance for τ ≥ 0.7, closely matching the BCJR algorithm, and offers computational gains of up to 46% and 84% over M-BCJR for BPSK and QPSK, respectively. Comparative analysis with other methods further highlights the efficiency and effectiveness of the proposed approach. To the best of our knowledge, this is the first application of a fixed-kernel CNN architecture tailored for FTN detection in the literature.