SavedAdd your journal

Journals / Avrupa Bilim ve Teknoloji Dergisi / 2026 / Sayı: 39 Özel Sayı

Machine Learning Based Classification Algorithm for AP Selection in Cell-Free MIMO Systems

Hücresiz ÇGÇÇ Sistemlerinde AP Seçimi için Makine Öğrenimi Tabanlı Sınıflandırma Algoritması

Mert Demirel(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0003-1035-6695
Esra Aycan(IZMIR KATIP CELEBI UNIVERSITY)0000-0001-7905-8456
Pages
71–75
Publication Date
2022-07-31
🏛️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)

As a result of the comparisons made, the best machine learning classification technique used has been proposed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the rapid development of technology, cellular networks in wireless networks are insufficient to meet the demands.
📊
Key Findings & Data:In order to provide a correct and good service to each user, communication systems must change.
💡
Conclusion & Impact:As a result of the comparisons made, the best machine learning classification technique used has been proposed.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hücresiz · Hücresiz ağ · Çoklu girişli çoklu çıkışlı sistem

Abstract

With the rapid development of technology, cellular networks in wireless networks are insufficient to meet the demands. In order to provide a correct and good service to each user, communication systems must change. Although cell-free networks have many advantages over cellular networks, since there are too many users and access points (APs) in cell-free networks, AP selection is very important. In this thesis, the AP selection model has been studied and compared five different machine learning classification methods. The campus of Izmir Katip Celebi University has been chosen as the environment where the study will be carried out, and capacity values have been obtained from the users and APs that have been placed on the campus in the simulation environment. Numerical calculation results have been obtained from the Wireless Insite (WI) software. The AP selection to be created with the capacity values has been supported by artificial intelligence algorithm techniques. With two different data sets have been compared, better results have been tried to be obtained with different feature values. As a result of the comparisons made, the best machine learning classification technique used has been proposed.

Özet

Teknolojinin hızlı gelişimi ile kablosuz ağlarda hücresel ağlar talepleri karşılamakta yetersiz kalmaktadır. Her kullanıcıya doğru ve iyi hizmet verebilmek için iletişim sistemlerinin değişmesi gerekmektedir. Hücresiz ağların hücresel ağlara göre birçok avantajı olmasına rağmen, hücresiz ağlarda çok fazla kullanıcı ve erişim noktası (AP) olduğundan, AP seçimi çok önemlidir. Bu tezde, AP seçim modeli incelenmiş ve beş farklı makine öğrenmesi sınıflandırma yöntemi karşılaştırılmıştır. Çalışmanın gerçekleştirileceği ortam olarak İzmir Katip Çelebi Üniversitesi kampüsü seçilmiş ve simülasyon ortamında kampüse yerleştirilen kullanıcı ve AP'lerden kapasite değerleri elde edilmiştir. Sayısal hesaplama sonuçları Wireless Insite (WI) yazılımından alınmıştır. Kapasite değerleri ile oluşturulacak AP seçimi yapay zeka algoritma teknikleri ile desteklenmiştir. İki farklı veri seti karşılaştırılmış, farklı öznitelik değerleri ile daha iyi sonuçlar elde edilmeye çalışılmıştır. Yapılan karşılaştırmalar sonucunda kullanılan en iyi makine öğrenmesi sınıflandırma tekniği önerilmiştir.

🔖

Cite This Article

Demirel, M., Aycan, E. (2022). Machine Learning Based Classification Algorithm for AP Selection in Cell-Free MIMO Systems. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 39 Özel Sayı, 71–75. https://doi.org/10.31590/ejosat.1142546

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Zhang, J., Chen, S., Lin, Y., Zheng, J., Ai, B., & Hanzo, L. (2019). Cell-free massive MIMO: A new next-generation paradigm. IEEE Access, 7, 99878-99888.
  2. Interdonato, G. (2020). Cell-Free Massive MIMO: Scalability, Signal Processing and Power Control (Vol. 2090). Linköping University Electronic Press.
  3. Ngo, H. Q., Ashikhmin, A., Yang, H., Larsson, E. G., & Marzetta, T. L. (2017). Cell-free massive MIMO versus small cells. IEEE Transactions on Wireless Communications, 16(3), 1834-1850.
  4. E. Nayebi, A. Ashikhmin, T. L. Marzetta and H. Yang, "Cell-Free Massive MIMO systems," 2015 49th Asilomar Conference on Signals, Systems and Computers, 2015, pp. 695-699, doi: 10.1109/ACSSC.2015.7421222.
  5. Björnson, E., & Sanguinetti, L. (2019, July). Cell-free versus cellular massive MIMO: What processing is needed for cell-free to win?. In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1-5). IEEE.
  6. Buzzi, S., D’Andrea, C., Zappone, A., & D’Elia, C. (2019). User-centric 5G cellular networks: Resource allocation and comparison with the cell-free massive MIMO approach. IEEE Transactions on Wireless Communications, 19(2), 1250-1264.
  7. Interdonato, G., Björnson, E., Quoc Ngo, H., Frenger, P., & Larsson, E. G. (2019). Ubiquitous cell-free massive MIMO communications. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1-13.
  8. Chen, S., Zhang, J., Björnson, E., Zhang, J., & Ai, B. (2020). Structured massive access for scalable cell-free massive MIMO systems. IEEE Journal on Selected Areas in Communications, 39(4), 1086-1100.
  9. Shakya, I. L., & Ali, F. H. (2020). Joint Access Point Selection and Interference Cancellation for Cell-Free Massive MIMO. IEEE Communications Letters, 25(4), 1313-1317.
  10. Buzzi, S., & D’Andrea, C. (2017). Cell-free massive MIMO: User-centric approach. IEEE Wireless Communications Letters, 6(6), 706-709.
  11. E. Bjornson and L. Sanguinetti, “Making cell-free massive MIMO competitive with MMSE processing and centralized implementation,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 77–90, Jan. 2020.
  12. Biswas, S., & Vijayakumar, P. (2021, March). AP selection in Cell-Free Massive MIMO system using Machine Learning Algorithm. In 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 158-161). IEEE.
  13. Rajapaksha, N., Manosha, K. S., Rajatheva, N., & Latva-Aho, M. (2021, June). Deep Learning-based Power Control for Cell-Free Massive MIMO Networks. In ICC 2021-IEEE International Conference on Communications (pp. 1-7). IEEE.