Waveform Decision Method with Machine Learning for 5G Uplink Communications

Year 2023, Volume: 15 Issue: 2, 820 - 827, 14.07.2023

Ayça HANÇER Ahmet YAZAR

https://doi.org/10.29137/umagd.1298404

Abstract

Prior to 5th generation (5G) communications systems, only a single waveform was used in the cellular communications uplink. In 5G communications systems, the standards of which were released for the first time in 2018, the flexibility of using two different waveforms for the uplink has been introduced. However, the methods to ensure the management of this flexibility are left to the designer in the standards. In this study, a novel machine learning-based method is developed for uplink waveform selection in 5G communications systems. The proposed method is designed to have a high level of environmental awareness.

Keywords

5G, Environment awareness, Waveform, Machine learning, Uplink communications

5G Yukarı Link Haberleşmesinde Makine Öğrenmesi ile Dalga Şekli Karar Yöntemi

Abstract

5. Nesil (5G) iletişim sistemleri öncesinde hücresel haberleşme yukarı linkinde sadece tek bir dalga şekli kullanılmaktaydı. İlk kez 2018 yılında standartları yayımlanmaya başlayan 5G iletişim sistemlerinde ise yukarı link için iki farklı dalga şekli kullanılabilmesi esnekliği getirilmiştir. Bununla beraber, bu esnekliğin yönetimini sağlayacak metotlar standartlarda geliştiriciye bırakılmıştır. Bu çalışmada, 5G iletişim sistemlerindeki yukarı link dalga şeklinin seçtirilmesine yönelik makine öğrenmesi tabanlı özgün bir metot geliştirilmiştir. Geliştirilen bu metot, çevresel farkındalığı yüksek seviyede olacak şekilde tasarlanmıştır.

Keywords

5G, Çevresel farkındalık, Dalga şekli, Makine öğrenmesi, Yukarı link haberleşmesi

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Thanks

Bu çalışma 122E400 no'lu Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) projesi kapsamında desteklenmiştir.

References

• 3GPP. 2022. "NR; Physical channels and modulation", Teknik Rapor, TR 38.211.
• Abd El-Hamid, H. E. M. A. A., Khalifa, W., Roushdy, M. I., Salem, A. M. 2019. "Machine Learning Techniques for Credit Card Fraud Detection", Future Computing and Informatics Journal, 4(2), 98-112.
• Correia, N., Al-Tam, F., Rodriguez, J. 2021. "Optimization of Mixed Numerology Profiles for 5G Wireless Communication Scenarios", Sensors, 21(4), 1-22.
• Dang, S., Amin, O., Shihada, B., Alouini, M. 2020. "What should 6G be?", Nature Electronics, 3(1), 20–29.
• El Emam, K. 2020. Accelerating AI with Synthetic Data (1. Basım). ABD: O'Reilly Media.
• Femenias, G., Riera-Palou, F., Mestre, X., Olmos, J.J. 2017. “Downlink scheduling and resource allocation for 5G MIMO-multicarrier: OFDM vs FBMC/OQAM”, IEEE Access, 5(1), 13770–13786.
• International Telecommunication Union (ITU). 2015. "IMT Vision – Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond", ITU Publications, M.2083-0.
• Kotagiri, D., vd. 2022 "Context-based Mixed-Numerology Profile Selection for 5G and Beyond", IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 611-616.
• Lee, H., Vahid, S., Moessner, K. 2019. "Machine Learning Based RATs Selection Supporting Multi-connectivity for Reliability", International Conference on Cognitive Radio Oriented Wireless Networks, 31-41.
• Marijanovic, L., Schwarz, S., Rupp, M. 2018. "Optimal Numerology in OFDM Systems Based on Imperfect Channel Knowledge", IEEE 87th Vehicular Technology Conference (VTC Spring), 1-5.
• Mathur, R.P., Pratap, A., Misra, R. 2017. “Distributed algorithm for resource allocation in uplink 5G networks”, MobiMWareHN, Mobility, Interf. Middlew. Manag. HetNets, 1–6.
• Nikolenko, S. I. 2021. Synthetic Data for Deep Learning (1. Basım). Springer.
• Saad, W., Bennis, M., Chen, M. 2020. "A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems", IEEE Network, 34(3), 134-142.
• Tang, F., Zhou, Y., Kato, N. 2020. “Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet”, IEEE J. Sel. Areas Commun., 38(12), 2773-2782.
• Yang, L., Shami, A. 2020. "On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice", arXiv:2007.15745 [cs.LG].
• Yarkan, S., Arslan, H. 2008. "Exploiting Location Awareness toward Improved Wireless System Design in Cognitive Radio", IEEE Communications Magazine, 46(1), 128–136. Yazar, A., Onat, F. A., Arslan, H. 2016. "New Generation Waveform Approaches for 5G and Beyond", IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU).
• Yazar, A., Arslan, H. 2018. “A Flexibility Metric and Optimization Methods for Mixed Numerologies in 5G and Beyond”, IEEE Access, 6(1), 3755-3764.
• Yazar, A., Arslan, H. 2019. "Selection of Waveform Parameters Using Machine Learning for 5G and Beyond", IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 1-6.
• Yazar, A., Dogan-Tusha, S., Arslan, H. 2020. “6G Vision: An Ultra-Flexible Perspective”, ITU Journal on Future and Evolving Technologies – Volume 2020, Article 9, 1(1), 1-20.
• Yazar, A., Arslan, A. 2020a. "A Waveform Parameter Assignment Framework for 6G With the Role of Machine Learning", IEEE Open Journal of Vehicular Technology, 1(1), 156-172.
• Yazar, A., Arslan, H. 2020b. "Introduction to Waveform Design", Flexible and Cognitive Radio Access Technologies for 5G and Beyond, The Institution of Engineering and Technology (IET), 3-27.
• Yazar, A. 2021. “Requirement Analysis and Clustering Study for Possible Service Types in 6G Communications”, IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU).
• Yu, T., Zhu., H. 2020. "Hyper-Parameter Optimization: A Review of Algorithms and Applications", arXiv:2003.05689 [cs.LG].
• Zhang, Z., vd. 2019. "6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies", IEEE Vehicular Technology Magazine, 14(3), 28-41.
• Zong, B., Fan, C., Wang, X., Duan, X., Wang, B., Wang, J. 2019. "6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies", IEEE Vehicular Technology Magazine, 14(3), 18-27.