Page 126 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue
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ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1




          processing), the O-RAN Distributed Unit (O-DU) is    perform    policy   management,     ML     model
          in  charge  of  the  High-PHY  layer  processing  (e.g.   management (described below in more detail) and
          modulation,  channel  coding),  Medium  Access       delivery  of  enriched  information  for  near-RT  RIC
          Control (MAC) and Radio Link Control (RLC), the O-   operation  (e.g.  RAN  data  analytics  that  could  be
          RAN Central Unit - Control Plane (O-CU-CP) hosts     exploited  by  the  near-RT  RIC).  Furthermore,
          the upper layers of the control plane radio protocol   complementing  the  A1  interface,  the  interactions
          stack, i.e. Radio Resource Control (RRC) and control   between  the  SMO  and  the  underlying  RAN  nodes
          plane of Packet Data Convergence Protocol (PDCP),    also  rely  on  the  adoption  of  other  standardized
          and the O-RAN Central Unit - User Plane (O-CU-UP)    interfaces  named  as  O1  and  O2  in  Fig.  1.  In
          handles the upper layers of the user plane protocol   particular,  O1  refers  to  the  set  of  service-based
          stack, i.e. Service Data Adaptation Protocol (SDAP)   management  interfaces  being  standardized  by
          and user plane of PDCP layers. Then, sitting on top   3GPP  for  configuration,  performance  and  fault
          of these RAN nodes handling the distributed radio    management of the RAN functionality [41]. In turn,
          protocol  stack,  there  is  the  near-real-time  RAN   the O2 interface supports  the management of the
          Intelligent Controller (near-RT RIC), which serves   cloud  infrastructure  and  resources  allowing  the
          as the brain of the RAN by coping with the different   execution of virtualized RAN functions.
          Radio  Resource  Management  (RRM)  functions        Building upon such a RAN reference architecture,
          needed  for  overall  RAN  operation,  such  as  radio   Fig.  2  shows  the  main  components  and  relations
          connection, mobility, Quality of Service (QoS) and   being delineated under O-RAN for the training and
          interference  management.  With  respect  to  the    deployment  of  ML-assisted  solutions  within  the
          interfaces between these RAN nodes, E1, F1-c and     SMO layer and/or within the RAN nodes themselves.
          F1-u interfaces are specified by 3GPP while Open
          fronthaul and E2 are being specified by the O-RAN                                ML Training Host
          Alliance.                                                Off-line data for
                                                                   model training
                                                                                             ML Training
                  Service management and orchestration (SMO)                                (and evaluation)
                              Non-RT RIC
                                                                                 ML model deployment

                             A1                       O1                                  ML Inference Host
                                                                          Online data for
             O2                                                           model inference
                               Near-RT RIC
                                                                                             ML Inference

                           O-CU-CP                                                                Outputs
                 E2       (RRC, PDCP)  E1  O-CU-UP                 Data Collection            Actor
                                         (SDAP, PDCP)                and pre-
                               F1-c                                 processing
                                             F1-u
                                                                                             ML-Assisted
                             O-DU: RLC/MAC/PHY-high                                           Solution
                   Open FrontHaul
                              O-RU: PHY-low / RF                Data:                 Actions:
                                                                Management and operational   -Internal actions within the actor
                                                                data (e.g. performance   (i.e. subject of action is the actor itself)
                               O-Cloud                          measurements, number of   -Configuration management over O1
                                                                connected devices, alarms)   -Policy management over A1
                   Fig. 1 – O-RAN functional architecture       from RAN nodes and UEs  -Control actions over E2
          Moving  at  the  management  plane,  O-RAN  defines
          the Service Management and Orchestration (SMO)        Fig. 2 – Components and relations for ML-assisted solutions
          layer,  which  actually  represents  the  Operations                    within O-RAN
          Support  Systems  (OSS)  of  the  MNO  for  the  RAN   As  shown  in  Fig.  2,  a  variety  of  management  and
          domain. As part of the SMO layer, O-RAN basically    operational data is collected from the different RAN
          defines the role of a non-real-time RAN Intelligent   nodes and User Equipment (UE) devices.  Such data,
          Controller (non-RT RIC) entity for the interaction   properly preprocessed, is used to feed the two key
          with the near-RT RIC via the A1 interface, which is   components  of  the  ML  processing  workflow,
          also  being  standardized  by  the  O-RAN  Alliance.   denoted  as  the  ML  training  host  and  the  ML
          Through the A1 interface [40], the non-RT RIC can    inference host. The ML training host represents the





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