SUN’IY INTELLEKT VA MATEMATIK MODELLASHTIRISHGA ASOSLANGAN RAQAMLI SIGNALLARNI QAYTA ISHLASHDA ILG‘OR SHOVQINNI KAMAYTIRISH USULLARI
Ключевые слова:
sun’iy intellekt, shovqinni kamaytirish, raqamli signalni qayta ishlash, FIR, IIR, neyron tarmoqlar, adaptiv filtrlarАннотация
Ushbu maqolada radiokanallardagi shovqin tufayli signal sifatining pasayishi va bit xato darajasi ning oshishi muammosi tahlil qilindi. Shovqin manbalari sifatida termik shovqin, atmosferik shovqin, interferensiya va ko‘p yo‘lli tarqalish effekti ko‘rib chiqilgan. Muammoni yechish uchun matematik modellashtirish, filtrlar va sun’iy intellekt modellaridan foydalanish asosida shovqinli nochiziqli signalini tozalash va original signalni tiklash jarayoni tahlil qilingan. Shovqinli raqamli signalni qayta ishlash va uning sifat ko‘rsatkichlarini yaxshilash maqsadida 1D CNN modeliga asoslangan dasturiy yechim ishlab chiqilgan. Modelning tuzilishi, o‘qitish bosqichlari hamda signalni filtrlash jarayonidagi vizual va miqdoriy natijalari tahlil qilingan. O‘tkazilgan tajribalar asosida taklif etilgan yondashuv bit xato darajasini sezilarli kamaytirishi va radio, mobil aloqa tizimlarida signal sifati oshishiga xizmat qilishimumkinligini keltirilgan.
Скачивания
Библиографические ссылки
1. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. https://doi.org/10.1007/978-0-387-45528-0
2. Goldsmith, A. (2005). Wireless communications. Cambridge University Press. https://doi.org/10.1017/CBO9780511841224
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org
4. Haykin, S. (2002). Adaptive filter theory (4th ed.). Prentice Hall.
5. O’Shea, T. J., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563–575. https://doi.org/10.1109/TCCN.2017.2758370
6. Proakis, J. G., & Salehi, M. (2008). Digital communications (5th ed.). McGraw-Hill.
7. Simon, D. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Wiley-Interscience.
8. Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. https://doi.org/10.1109/TIP.2017.2662206
Загрузки
Опубликован
Выпуск
Раздел
Лицензия
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
License Terms of our Journal
