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               HỘI THẢO QUỐC TẾ ATiGB LẦN THỨ CHÍN - The 9  ATiGB 2024                                 145

               strict  latency-sensitive  applications  which  require   [9]. D.  Marutho,  S.  Hendra  Handaka,  E.  Wijaya,  Muljono,  “The
               real-time,  Quality-of-Experience-based  optimization.   Determination  of  Cluster  Number  at  k-Mean  Using  Elbow
               It  is  obvious  that  more  capacity,  less  latency   Method  and  Purity  Evaluation  on  Headline  News”,  in:  Proc.
                                                                 Int’l  Seminar  on  Application  for  Technology  of  Information
               synchronization  and  stronger  resilience  MBH  is   and Communication, ISEMANTIC, 2018, pp. 533-538.
               essential  to  realize  ultra-reliable  low  latency   [10].F. Liu, Y. Deng, “Determine the Number of Unknown Targets
               communication   [12].   Consequently,   timely    in  Open  World  Based  on  Elbow  Method”,  IEEE  Trans.  on
               identification and localization of BSs’ KPI-degraded   Fuzzy Systems. 29 (5) (2021) 986-995.
               backhaul for correcting is very important.     [11].K.  P.  Sinaga,  M.  Yang,  “Unsupervised  K-Means  Clustering
                                                                 Algorithm”, IEEE Access. 8 (2020) 80716-80727.
                  5. CONCLUSION
                                                              [12].Berke  Tezergil,  Ertan  Onur,  “Wireless  Backhaul  in  5G  and
                  This  paper  proposes  an  approach  utilizing   Beyond:  Issues,  Challenges  and  Opportunities”,  IEEE
               unsupervised  learning,  specifically  Elbow  method   Communications  Surveys  &  Tutorials.  24(4)  (2022).  2579  -
               combined  with  the  k-means  clustering  algorithm  to   2632.
               uncover  hidden  performance  patterns  of  the  MBH
               from a real dataset. With the obtained results, MNOs
               can  optimally  classify  groups  of  BSs  that  have
               kindred backhaul transmission attributes in respect of
               PL  and  TWAMP  setup  success  rate  in  order  to
               readjust  properly  and  reasonably  BSs  having  bad
               transmission  quality.  According  to  the  results  of
               clustering  MBH  data  through  the  proposed  method,
               the group of BSs that belong to the cluster with poor
               transmission quality will be preferentially troubleshot
               in order to leverage quality of services and network
               capabilities. We can apply the suggested method into
               other clustering problems and reflect other behaviors
               arising  in  the  mobile  network  to  have  a  complete
               overview of the network’s performance for devising
               fair-minded technical solutions.
                              REFERENCES
               [1]. Amir Ashtari Gargari, Matteo Pagin, Michele Polese, Michele
                  Zorzi,  “6G  Integrated  Access  and  Backhaul  Networks  with
                  Sub-Terahertz  Links”,  2023  18th  Wireless  On-Demand
                  Network  Systems  and  Services  Conference  (WONS).  (2023)
                  13-19.
               [2]. Ahmed  Abdelmoaty,  Diala  Naboulsi,  Ghassan  Dahman,
                  François Gagnon, “When Resiliency Matters: An Overview of
                  5G  and  Beyond  Wireless  Backhaul  Network  Design”.  IEEE
                  Communications Magazine. 61(12) (2023) 206 - 212.
               [3]. Kenneth  Y.  Ho,  Esa  Metsälä,  “Key  5G  Transport
                  Requirements”. Wiley Telecom, (2023) 65 - 103.
               [4]. Ibrahim Alhassan Gedel, Nnamdi I. Nwulu, “Low Latency 5G
                  IP  Transmission  Backhaul  Network  Architecture:  A  Techno-
                  Economic  Analysis”,  Wireless  Communications  and  Mobile
                  Computing, 2024.
               [5]  T.  –C.  Chuang,  M.  Liu,  Y.  -Z.  Haung,  C.  –W.  Yang,  “An
                  Integrated Monitoring System for 5G Crosshaul Network”, in:
                  Proc. 21st Asia-Pacific Network Operations and Management
                  Symposium, APNOMS, 2020, pp. 338–340.
               [6]. Y.  Türk,  E.  Zeydan,  “A  TWAMP  Coordinated  Data
                  Compression System for 5G Backhaul”, in: Proc. IEEE Conf.
                  on Network Softwarization, NetSoft, 2019, pp. 245-247.
               [7]. C.  Pham-Quoc,  T.  Tang-Anh,  “Predicting  mobile  networks’
                  behaviors  using  Linear  Neural  Networks”,  in:  Proc.  IEEE
                  Eighth Int’l Conf. on Communications and Electronics, IEEE
                  ICCE, 2021, pp. 75-79.
               [8]. Paolo  Di  Prisco;  Antti  Pietiläinen;  Juha  Salmelin,  "Wireless
                  Backhaul  and  Fronthaul,"  in  5G  Backhaul  and  Fronthaul  ,
                  Wiley, 2023, pp.165-189.

                                                                                   ISBN: 978-604-80-9779-0
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