Deep Learning-Based Target Parameter Estimation in MIMO-OFDM ISAC Systems Derin Ö?grenme Tabanli MIMO-OFDM ISAC Sistemlerinde Hedef Parametre Kestirimi
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11637044
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: CFAR, Deep Learning, ISAC, OFDM
- Kocaeli Üniversitesi Adresli: Evet
Özet
In this study, the problem of estimating the target angle, range, and velocity parameters in MIMO-OFDM based Integrated Sensing and Communication (ISAC) systems is investigated. The conventional three-dimensional Cell Averaged Constant False Alarm Rate (3D CA-CFAR) algorithm, widely used in radar-based approaches, suffers from limited estimation accuracy and high computational complexity, particularly under low signal-to-noise ratio (SNR) conditions. Motivated by this limitation, a deep learning-based approach is proposed to estimate target parameters through regression by directly processing ISAC detection matrices. In this study, models based on EfficientNet-B1, EfficientViT-B1, and SE-ResNeXt-26 architectures are trained on a synthetically generated dataset covering a wide SNR range and evaluated in comparison with the conventional 3D CA-CFAR method. The results demonstrate that the transformer-based EfficientViT-B1 model achieves lower estimation error and improved robustness, especially under low SNR conditions.