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  1. AU="Kose, Abdulkadir"
  2. AU=Matsumoto Kunio AU=Matsumoto Kunio
  3. AU="Mutus, Bulent"
  4. AU="Yu-Shan Gao"
  5. AU="Afza Gardner, Qurratulann"
  6. AU="Madan, Arina"
  7. AU="Cutshaw, Kaiyel A"
  8. AU="Al-Khazraji, Ahmed"
  9. AU="Mukherjee, Geetashree"
  10. AU="DeGraff, David J"
  11. AU="Schmoellerl, Johannes"
  12. AU="Henderson, S"
  13. AU="Carvalheiro, Tiago"
  14. AU="Bastos Soares, Taís Cristina"
  15. AU="Yin, Guo-Qing"
  16. AU="Sharber, Brian"
  17. AU="Goyaux, Nathalie"
  18. AU="Rauch, Geraldine"
  19. AU="Ekoh, Okwukwe Faith"
  20. AU="A Rahim, Haslinda"
  21. AU="Richard Stahl"
  22. AU=Vazquez-Iglesias J L
  23. AU="Amundsen, David S"
  24. AU="Konios, Dimitrios"
  25. AU="Lindh, Ingrid"
  26. AU=Zhao Chunyan
  27. AU="Scalia, Jennifer B"
  28. AU="Balint, Lajos"
  29. AU="Liang, Siping"
  30. AU="Wong, Anthony"
  31. AU="Müjdat YENİCESU"
  32. AU="Brooks, M L"
  33. AU="Garcia-Gutierrez, Ania" AU="Garcia-Gutierrez, Ania"
  34. AU="Marina Paola Gardiman"
  35. AU="Labarthe, Simon"
  36. AU="Jiahui Li"
  37. AU="Geier, Johannes"
  38. AU=Thangaraju Pugazhenthan
  39. AU="Tapio, Joona"
  40. AU="Navaratnam, Dhasakumar"
  41. AU="Blank, Marissa Cathleen"
  42. AU="Sadeghi, Yasaman"
  43. AU=Ye Keqiang
  44. AU="Huda, Walter"
  45. AU="Petrova, Polina E"
  46. AU="Pond, Gregory"
  47. AU=Krzewski Konrad
  48. AU="Feng, Yuquan"
  49. AU="Schmermund, Ben Niklas"
  50. AU="Soni, Payal D"
  51. AU="Romero-García, Carolina"
  52. AU="Petty, Lloyd"
  53. AU="James E. Posey"

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  1. Buch ; Online: Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications

    Kose, Abdulkadir / Lee, Haeyoung / Foh, Chuan Heng / Shojafar, Mohammad

    2024  

    Abstract: Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has ... ...

    Abstract Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.

    Comment: Accepted in IEEE Transactions on Intelligent Transportation Systems
    Schlagwörter Electrical Engineering and Systems Science - Signal Processing ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 003
    Erscheinungsdatum 2024-01-04
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Recent Advances in Machine Learning for Network Automation in the O-RAN.

    Hamdan, Mutasem Q / Lee, Haeyoung / Triantafyllopoulou, Dionysia / Borralho, Rúben / Kose, Abdulkadir / Amiri, Esmaeil / Mulvey, David / Yu, Wenjuan / Zitouni, Rafik / Pozza, Riccardo / Hunt, Bernie / Bagheri, Hamidreza / Foh, Chuan Heng / Heliot, Fabien / Chen, Gaojie / Xiao, Pei / Wang, Ning / Tafazolli, Rahim

    Sensors (Basel, Switzerland)

    2023  Band 23, Heft 21

    Abstract: The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, ... ...

    Abstract The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.
    Sprache Englisch
    Erscheinungsdatum 2023-10-28
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23218792
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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