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ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1





                   MACHINE LEARNING-ASSISTED CROSS-SLICE RADIO RESOURCE OPTIMIZATION:
                           IMPLEMENTATION FRAMEWORK AND ALGORITHMIC SOLUTION

                              Ramon Ferrús,  Jordi Pérez-Romero,  Oriol Sallent,  Irene Vilà,  Ramon Agustí
           Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), c/ Jordi Girona, 1-3, Barcelona, Spain

                                   NOTE: Corresponding author: Ramon Ferrús (ferrus@tsc.upc.edu)


          Abstract – Network slicing is a central feature in 5G and beyond systems to allow operators to customize
          their networks for different applications and customers. With network slicing, different logical networks, i.e.
          network  slices,  with  specific  functional  and  performance  requirements  can  be  created  over  the  same
          physical network. A key challenge associated with the exploitation of the network slicing feature is how to
          efficiently  allocate  underlying  network  resources,  especially  radio  resources,  to  cope  with  the  spatio-
          temporal traffic variability while ensuring that network slices can be provisioned and assured within the
          boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with customers. In this
          field, the use of artificial intelligence, and, specifically, Machine Learning (ML) techniques, has arisen as a
          promising approach to cater for the complexity of resource allocation optimization among network slices.
          This  paper  tackles  the  description  of  a  feasible  implementation  framework  for  deploying  ML-assisted
          solutions for cross-slice radio resource optimization that builds upon the work conducted by 3GPP and O-
          RAN Alliance. On this basis, the paper also describes and evaluates an ML-assisted solution that uses a Multi-
          Agent Reinforcement Learning (MARL) approach based on the Deep Q-Network (DQN) technique and fits
          within the presented implementation framework.

          Keywords – 5G, cross-slice resource optimization, deep learning, machine learning, network slicing

                                                               resources  efficiently  [7]-[14].  Remarkably,  the
          1.   INTRODUCTION
                                                               automation  of  the  life-cycle  management  of
          Network slicing allows operators to customize their   network  slices  in  the  RAN  requires  two  main
          networks for different applications and customers    functionalities:  slice  admission  control  and  cross-
          [1],  [2].  Slices  can  differ  in  functionality  (e.g.  air   slice resource optimization.
          interface capabilities, mobility tracking features), in   Slice admission control is needed to decide on the
          performance requirements (e.g. latency, availability,   acceptance or rejection of a new RAN slice creation
          reliability  and  data  rates),  or  they  can  serve  only   request  with  specific  coverage,  functional  (i.e.
          specific  users  (e.g.  public  safety  users,  corporate   features)  and  performance  (e.g.  service  quality,
          customers, or industrial users). A network slice can   capacity) requirements. Under Network as a Service
          provide  the  functionality  of  a  complete  network,   (NaaS)  business  models  such  as  neutral  host
          including radio access network  and  core network    services, the slice requirements will be determined
          functions.  Support  for  network  slicing  has  been   by  the  Service  Level  Agreement  (SLA)  /  Service
          introduced  by  the  3rd  Generation  Partnership    Level Specifications (SLS) established between the
          Project (3GPP) as part of the first release of the Fifth   service  provider  (e.g.  the  operator  of  a  RAN
          Generation (5G) system specifications (Release 15),   infrastructure  installed  in  a  venue)  and  the
          with multiple enhancements still to follow in future   customer (e.g. a Mobile Network Operator - MNO).
          releases,  as  reflected  by  different  study  items  in   The fulfillment of the RAN slice requirements may
          progress, such as [3]-[6].
                                                               result in the need to guarantee the availability of a
          The creation and management of network slices is     certain amount of radio resources to the new slice,
          especially challenging in the Radio Access Network   defined in terms of, e.g. number of Resource Blocks
          (RAN), where multiple slices can be delivered over   (RBs)  per  cell,  percentage  of  cell  capacity,  etc.
          the  same  radio  channel  and  the  system  shall   Therefore,  the  slice  admission  control  shall  estimate
          guarantee that the allocation and distribution of the   the  amount  of  radio  resources  required  by  the  new
          radio resources within the radio channel is done so   slice and decide whether this can be enforced given the
          that specific requirements per slice can be fulfilled   deployed  network  capacity  and  the  amount  of
          (e.g.  guaranteed  capacity)  while  using  radio    resources consumed by the already admitted slices.





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