Page 27 - Proceedings of the 2018 ITU Kaleidoscope
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Machine learning for a 5G future

























                                  Figure 3. Machine-learning techniques and paradigms in 5G [11]

           In  this  sense,  a  detailed  description  of  the  different  ML   In  this  context,  ITU-T  SG-13  has  developed  several
           algorithms and their application in the 5G environment is   Recommendations related to 5G QoS frameworks and ML
           presented in [11] (Figure 3). The authors of this paper note   techniques [4, 12] (Figure 4). In addition, ITU-T SG-12, the
           the  importance  of  selecting  the  appropriate  learning  type   expert  group  responsible  for  the  development  of
           when  using  ML  techniques  since  the  goal  of  ML  is  to   international standards for QoS and QoE, has been active in
           predict  the  output  of  an  input  turning  observational  data   the  editing  and  updating  of  related  Recommendations  to
           into a model that can be used for this prediction. Therefore,   work  around  new  mobile  scenarios  [6,  7].  The  5G-PPP
           depending on the nature of the observational data, different   partnership,  initiative  between  the  European  Commission
           types of learning can be distinguished:            and  European  ICT  industry,  is  also  working  actively  to
                                                              define  architectures,  technologies  and  standards  for  NGW
                 In  supervised  learning,  each  input  value  of  the  systems.
                  observational data is given with the corresponding
                  output  to  form  a  training  set.  This  set  is  used  to  In spite of the significant advances in the definition of the
                  learn a predictive function. Supervised learning is  5G  ecosystem  and  the  identification  of  new  QoE  models
                  useful  for  classification  and  for  regression  and  ML  techniques  for  this  environment,  there  is  still  a
                  problems.
                 In unsupervised learning, only the input values are  need for global QoS management models to be deployed in
                                                              real-world  NGW  scenarios.  In  the  following  sections,  an
                  included  in  the  observational  data.  The  most  approach to solving this shortage is presented.
                  common  application  of  unsupervised  learning  is
                  cluster  analysis  to  find  similarities  between  the  IMT-2020 QoS Monitoring and Data Collection
                  input  values and extract hidden patterns  to group     Static and dynamic QoS information data
                  them into clusters.                                   Machine learning based IMT-2020 assurance
                 Reinforcement  learning  is  based  on  dynamic               functional entity
                  iterative  learning  and  decision-making  processes.   Machine learning based IMT-2020 QoS
                  The  learner  is  not  told  which  actions  to  take  but   assurance modeling
                  instead must determine those that yield the output      Machine learning based IMT-2020 QoS
                  closest to the target by successive trials.                  assurance training
           2.2 Standardization: Key to 5G                                  IMT-2020 QoS anomaly detector

                                                                           IMT-2020 QoS anomaly predictor
           Standardization  bodies  also  envision  the  importance  of
           defining  new  standards  on  QoS  and  ML  for  NGW           IMT-2020 QoS reporting of QoS anomaly
           ecosystems.  In  January  2018,  a  workshop  on  "Machine       detection and prediction results  IMT-2020
           Learning  for  5G  and  beyond"  was  held  in  Geneva    IMT-2020 Control Plane      Management Plane
           (Switzerland)  in  the  context  of  the  first  meeting  of  the   IMT-2020 QoS Control Plane Function  QoS Management
                                                                                                    IMT-2020
                                                                                   Anomaly
           recently-launched ITU “Focus Group on Machine Learning    QoS Data   QoS Control   Detection   QoS Capabilities   Plane Function
                                                                            Modeling
                                                                                          Exposure
                                                                     Collection
           for  Future  Networks  including  5G”  [5].  Three  different   &Training  &Prediction   Interface  IMT-2020 QoS
                                                                                                     Policy
           working groups were established:                          IMT-2020 User Plane            IMT-2020
                                                                                                      SLA
                WG1: Use cases, services & requirements             IMT-2020 QoS User Plane Function  QoS  QoS  IMT-2020 Plane
                                                                             QoE
                                                                                                    QoS Function
                                                                       QoS
                WG2: Data formats & ML technologies                  Mapping  Awareness  Enforcement  Monitoring  Management
                WG3: ML-aware network architecture
                                                               Figure 4. ITU model of ML-based QoS assurance [4, 12]

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