Sun'iy intellekt va telekommunikatsiya integratsiyasi: zamonaviy aloqa tizimlarining rivojlanishidagi yangi bosqich
Annotatsiya
Zamonaviy aloqa tizimlari jadal rivojlanib, sun'iy intellekt (AI) texnologiyalari bilan tobora chuqur integratsiyalashmoqda. Ushbu maqolada sun'iy intellekt va telekommunikatsiyaning birlashishi natijasida vujudga kelayotgan yangi avlod tarmoqlari, jumladan 5G va 6G texnologiyalari, tarmoq avtomatizatsiyasi hamda xizmat sifatini (QoS) oshirishdagi innovatsion yondashuvlar tahlil etiladi. Tadqiqotda mashinaviy o'qitish (ML) algoritmlari, xususan LSTM, Random Forest va XGBoost modellarining telekommunikatsiya sohasidagi amaliy qo'llanilishi ko'rib chiqiladi. 50 000 ta kuzatuv nuqtasidan iborat dataset ustida o'tkazilgan eksperimentlar shuni ko'rsatdiki, LSTM modeli 93,1% aniqlik va 47 daqiqalik oldindan ogohlantirish imkoniyati bilan eng yuqori samaradorlikka erishdi. Maqola AI asosidagi telekommunikatsiya infratuzilmasining kelajakdagi rivojlanish istiqbollarini ham muhokama qiladi.
Kalit so'zlar: sun'iy intellekt, telekommunikatsiya, mashinaviy o'qitish, LSTM, 5G, 6G, tarmoq avtomatizatsiyasi, QoS, anomaliyalarni aniqlash, tarmoq monitoringi
Библиографические ссылки
1. International Telecommunication Union (ITU). Measuring digital development: Facts and Figures 2023. ITU Publications, Geneva, 2023.
2. Springer AI Review. Artificial intelligence advances in anomaly detection for telecom networks. Artificial Intelligence Review. Springer Nature. Jan. 2025. DOI: 10.1007/s10462-025-11108-x
3. Machine learning-based network anomaly detection: Design, implementation, and evaluation. AI, vol. 5, no. 4, pp. 2967-2983, Dec. 2024. MDPI. DOI: 10.3390/ai5040143
4. Ren H., Wang R., Dong W., Li J., Tang Y. Dynamic resampling based boosting random forest for network anomaly traffic detection. Proc. IEA/AIE 2023. Springer. 2023. pp. 329-340.
5. Otajonov S., Khalilov M., Juraev N., Djalilov B. Effects of the interaction of chlorine with PbTe surface. E3S Web of Conferences (Vol. 587, p. 01001). EDP Sciences. 2024.
6. Bikkasani D.C., Yerabolu M.R. AI-driven 5G network optimization. American Journal of Artificial Intelligence, vol. 8, no. 2, pp. 55-62. 2024.
7. Gudepu V. et al. Generative-AI for AI/ML model adaptive retraining in beyond 5G networks. arXiv:2408.14827. Aug. 2024.
8. Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press. 2016. 800 p.
