Page 124 - 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




          Once multiple slices have been activated in the RAN,   paid  to  the  practical  implementation  aspects  of
          the  cross-slice  resource  optimization  shall  ensure   these  solutions,  as  it  will  be  further  discussed  in
          that the slice requirements are satisfied over time   Section 2. In this respect, departing from 3GPP and
          and RAN resources are efficiently utilized. This may   O-RAN Alliance specifications, a first contribution of
          imply  a  dynamic  modification  of  the  slice      this  paper  is  the  delineation  of  the  functional
          configurations (e.g. specifying the amount of radio   framework and information models to be accounted
          resources  assigned  to  each  slice  at  each  cell,   when  targeting  a  practical  realization  of  ML-
          adjustment  of  slice-aware  scheduling  settings,   assisted  cross-slice  radio  resource  optimization
          configuration  of  rate  limiters,  bandwidth  parts,   solutions  for  5G  and  beyond  systems.  More
          mobility load balancing parameters, access control   specifically,  the  focus  is  put  here  on  the
          priorities, etc.) during its lifetime in order to deal   identification of the specific functional components
          with the dynamics of the traffic load of the slice and   enabling the deployment of ML-based solutions for
          with  the  random  propagation  effects  that  lead  to   RAN management along with the set of information
          non-deterministic mapping between radio resource     models that have been defined to represent SLAs,
          consumption  and  performance  requirements.         network slice instances’ characteristics and slicing-
          Cross-slice  resource  optimization  has  been       related  configuration  parameters  of  5G  base
          identified by 3GPP as a use case in the context of   stations. On this basis, a second contribution of this
          Self-Organizing  Network  (SON)  feasibility  studies   paper  is  the  formulation  and  assessment  of  a
          [15], addressing not only the dynamic allocation of   plausible  ML-assisted  cross-slice  radio  resource
          radio resources to slices but also the distribution of   optimization solution that fits within the delineated
          other resources such as storage and computing for    implementation  framework.  The  solution  makes
          virtualized implementations.                         use of Multi-Agent Reinforcement Learning (MARL)
                                                               based  on  the  Deep  Q-Network  (DQN)  technique.
          The decision-making logic for cross-slice resource
          optimization  needs  to  deal  with  a  lot  of      Illustrative  performance  results  of  the  proposed
          uncertainties  and  random  processes  associated    solution are provided by means of simulations.
          with  the  variability  in  traffic  generation,  device   The  rest  of  the  paper  is  organized  as  follows.
          mobility and radio channel conditions, so it is highly   Section 2 presents an overview of related works in
          difficult  to  have  an  accurate  a  priori  statistical   order to position the paper in relation to the state-
          knowledge of the network resource utilization and    of-the-art. Section 3 describes the implementation
          delivered performance. For this reason, model-free   framework, which is particularized to the proposed
          Machine  Learning  (ML)-based  methods,  which  do   ML-assisted  cross-slice  optimization  solution  in
          not rely on predefined models but are able to learn   Section 4 and Section 5 presents some illustrative
          and/or predict the particular network dynamics as    proof-of-concept  results.  Finally,  our  concluding
          well  as  to  operate  under  goal-oriented  policies,   remarks are wrapped up in Section 6.
          become  adequate  solutions  to  the  problem  [16].       RELATED WORK
          Besides, the complexity of the problem with a huge   2.
          number of variables and conditions (e.g. particular   Artificial Intelligence (AI) and more specifically ML
          device  capabilities,  pending  traffic,  link  channel   techniques have been applied in the literature for
          conditions, resource consumption, etc.) also pushes   both slice admission control and cross-slice radio
          for the introduction of these sorts of methods. As a   resource allocation. In the area of slice admission
          result,  the  system  can  be  in  a  large  number  of   control,  [17]  studied  an  optimal  algorithm  using
          possible  states  in  which  the  cross-slice  resource   Semi-Markov Decision Processes (SMDP) and then
          allocation needs to determine the optimum capacity   proposed  an  adaptive  algorithm  based  on  Q-
          sharing  among  slices.  In  this  case,  among  the   learning. Then, other works have considered deep
          possible  ML  techniques,  deep  reinforcement       Q-learning [18] along with variants for enhancing
          learning (RL) schemes become particularly relevant   the  training  process,  such  as  deep  dueling  neural
          because  they  provide  faster  convergence  under   networks  [19].  ML  tools  have  also  been  used  for
          large  state/action  spaces  in  comparison  with    enhancing  the slice  admission  control  with  traffic
          classical reinforcement learning.                    prediction,  such  as  in  [20],  [21],  which  use  Holt-
                                                               Winters  prediction,  or  [22],  which  uses  a
          While  there  is  a  significant  amount  of  work
          addressing  the  cross-slice  resource  optimization   combination of Long Short Term Memory (LSTM)
          problem  from  an  algorithmic  and  performance     and  dense  neural  networks  for  predicting  the
          assessment  perspective,  less  attention  has  been   resource usage.





          104                                © International Telecommunication Union, 2020
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