SavedAdd your journal

Journals / EMO Bilimsel Dergi / 2026 / Cilt: 13 Sayı: 2

Performance of Machine Learning-based Water-Filling Algorithm for Energy Efficient Data Transmission over Different Fading Channels in Wireless Networks

Kablosuz Ağlarda Farklı Gölgelenen Kanallar Üzerinden Enerji-Verimli Veri İletimi için Makine Öğrenmesi-temelli Su-Doldurma Algoritması

Ömer Melih Gül(BAHÇEŞEHİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-0673-7877
Pages
81–89
Publication Date
2023-07-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, we tackle a resource allocation problem over multiple fading channels in wireless networks.
📊
Key Findings & Data:Differing from previous studies, the data transmission rate can take a value out of a discrete set of data transmission rates in this work.
💡
Conclusion & Impact:The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Su-Doldurma · Makine Öğrenmesi · Çevrimiçi Politika

Abstract

In this paper, we tackle a resource allocation problem over multiple fading channels in wireless networks. Differing from previous studies, the data transmission rate can take a value out of a discrete set of data transmission rates in this work. We propose machine-learning-based online waterfilling algorithms for this problem. The relative performance of the online and optimal offline policies are evaluated for various types of fading channels (Rayleigh, Rician, Nakagami, Weibull) over various time horizons. The numerical results demonstrate these online waterfilling algorithms shows close performance to offline waterfilling algorithms especially for longer time horizons.

Özet

Bu çalışmada, kablosuz ağlarda çoklu gölgelenen kanallar üzerinden bir radio kaynak tahsisi problem ele alınmaktadır. Bu çalışmada önceki çalışmalardan farklı olarak, anlık veri iletim hızı sadece ayrık bir kümedeki veri iletim hızı değerlerinden birini alabilir. Bu probleme makine öğrenmesi-temelli çevrimiçi su doldurma algoritmaları önerilmiştir. Çevrimiçi ve eniyi çevrimdışı politikaların göreli performansı, çeşitli tiplerde (Rayleigh, Rician, Nakagami, Weibull) gölgelenen kanallar için çeşitli zaman ufuklarında değerlendirilmektedir. Sayısal sonuçlar, özellikle daha uzun zaman ufukları için, bu çevrimiçi su doldurma algoritmalarının çevrimdışı su doldurma algoritmalarına yakın performansı olduğunu göstermektedir.

🔖

Cite This Article

Gül, Ö. M. (2023). Performance of Machine Learning-based Water-Filling Algorithm for Energy Efficient Data Transmission over Different Fading Channels in Wireless Networks. EMO Bilimsel Dergi, Cilt: 13 Sayı: 2, 81–89. https://ajindex.com/articles/1390320

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (26)

  1. Qualcomm. Everything you need to know about 5G. Available at https://www.qualcomm.com/5g/what-is-5g
  2. Nazir, M., Sabah, A., Sarwar, S. et al. (2021). Power and Resource Allocation in Wireless Communication Network. Wireless Pers Commun 119, 3529-3552.
  3. Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
  4. Cover, T. and Thomas, J. (2006). Elements of Information Theory, 2nd Edition. Wiley&Sons.
  5. Dai, M., Zhang S., Chen, B., Lin, X., &Wang, H. (2014). A refined convergence condition for iterative waterfilling algorithm. IEEE Communications Letters, 18(2), 269-272.
  6. Gai, Y.& Krishnamachari, B. (2012). Online Learning Algorithms for Stochastic Water-Filling. IEEE Information Theory and Applications Workshop (ITA), 1-6.
  7. Goldsmith, A. (2005). Wireless Communications. Cambridge University Press.
  8. Goldsmith, A., &Varaiya, P. P. (1996). Capacity, mutual information, and coding for finite-state Markov channels. IEEE Transactions on Information Theory, 42 (3), 868-886.
  9. Tse, D. & Viswanath, P. (2005). Fundamentals of Wirelss Communication. Cambridge University Press.
  10. Teletar, E. (1995). Capacity of multi-antenna Gaussian channels. AT&T Bell Labs Internal Tech. Memo.
  11. Yang, J. and Roy, S. (1994). On joint transmitter and receiver optimization for multiple-input-multiple-output (MIMO) transmission systems. IEEE Transactions on Communications, 42(12), pp. 3221-3231.
  12. Xing, C., Jing, Y., Wang, S., Ma, S. & Poor, H. V. (2020). New Viewpoint and Algorithms for Water-Filling Solutions in Wireless Communications. in IEEE Transactions on Signal Processing, 68, 1618-1634.
  13. Ajitsinh. N., Jadhav1 and Sakib. R. Mujawar. (2017). Different power loading allocation schemes for ZF based cognitive radio system. International Journal of Latest Trends in Engineering and Technology, 8 (1), 350-359.
  14. Qi Q, Minturn A., and Yang Y. L. (2012). An Efficient Water-Filling Algorithm for Power Allocation in OFDM-Based Cognitive Radio Systems. 2012 International Conference on Systems and Informatics, 2069-2073.
  15. P. He, L. Zhao, S. Zhou, Z. Niu, (2013). Water-Filling: A Geometric Approach and its Application to Solve Generalized Radio Resource Allocation Problems. IEEE Transactions on Wireless Communications, 12 (7), 3637-3647.
  16. Noor Shahida M. K, Nordin R. and Ismail. M. (2017). Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions. Intl Journal of Electronics& Telecommunications, vol. 63, no. 1, pp. 79-84.
  17. Yu S., Daoxing G., Lu L., and Xiaopei D., "A modified water-filling algorithm of power allocation," in Information Technology, Networking, Electronic &Automation Control Conf., IEEE, 2016.
  18. Wael C. B. A, Armi N., Miftahushudur M. T., Muliawarda D., and Sugandi G., "Power Allocation in OFDM-Based Cognitive Radio Networks for Fading Channel," in 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2017.
  19. Zeng M., Nguyen N. P., Dobre O. A., Ding Z., and Poor H. V., "Spectral- and Energy-Efficient Resource Allocation for Multi-Carrier Uplink NOMA Systems," Vehicular Technology, IEEE Transactions on, 2019.
  20. Elgarhy O. and Reggiani L., "Application of the Water Filling Algorithm to the Sum Rate Problem with Minimum Rate and Power Constraint," 2018 Advances in Wireless and Optical Communications (RTUWO), 2018, pp. 12-16.
  21. Qian L. P., Zhang Y. J., and Huang J., “MAPEL: Achieving global optimality for a non-convex wireless power control problem,” IEEE Trans. on Wireless Communications, vol. 8, no. 3, pp. 1553-1563, Mar. 2009.
  22. Kim Y.; Kang M.; Varshney L. R.; Shanbhag N. R., (2018). Generalized Water-Filling for Source-Aware Energy-Efficient SRAMs. IEEE Trans. on Communications, 66 (10), 4826-4841.
  23. Gurdasani H., Ananth, A. G., Thangadurai N. (2021). Channel Capacity Enhancement of MIMO System using Water-Filling Algorithm. Turkish Journal of Computer and Mathematics Education.12 (12), 192-201.
  24. Wei S., Zheng Z. and Wu, C., Channel Power Allocation Optimization Based on Water-filling Algorithm in 5G. J. Phys.: Conf. Ser. 1871 012082.
  25. Gul, O. M. (2022). Performance of History-based Water-Filling Algorithm for Energy-Efficient Data Transmission over Different Fading Channels. Avrupa Bilim ve Teknoloji Dergisi, (41), 118-125. DOI: 10.31590/ejosat.1112389.
  26. Gul, O. M. (2022). Performance of History-based Water-Filling Algorithm for Energy-Efficient Data Transmission over Different Fading Channels. Avrupa Bilim ve Teknoloji Dergisi, (41), 118-125. DOI: 10.31590/ejosat.1112389.