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




          In  the  field  of  cross-slice  optimization,  different   Another  important  novelty  comes  from  the
          approaches  exist  exploiting  several  ML  tools.   specification of the SLA terms for a RAN slice to be
          Q-learning  was  used  in  [23]  to  design  a  slicing   used by the ML-based solution. This paper takes as
          controller  that  decides  which  resource  units  are   a  reference  the  attributes  defined  in  the  GSMA
          allocated to each slice based on requirements at the   Generic  Slice  Template  considered  by  3GPP  to
          user level. Q-learning complemented with a genetic   specify the SLA to be fulfilled for a RAN slice across
          algorithm was considered in [24] for scaling down    a geographical area covering multiple cells in terms
          allocated resources to slices for congestion control   of, e.g. the total amount of capacity to be provided
          purposes. In [25] deep deterministic policy gradient   to  each  slice.  Instead,  other  approaches  such  as
          (DDPG)  is  used  to  allocate  resource  blocks  to   [28]-[32] just consider the SLA specified in terms of
          different  tenants  in  a  cloud  RAN  environment.  In   the QoS parameters defined at  the user level, but
          turn,  game  theory  with  exponential  learning  is   without enforcing any aggregate capacity per slice.
          proposed in [26] to divide the network resources     Finally, another difference with respect to previous
          (i.e.  bandwidth)  among  slices  using  OpenFlow,   works  comes  from  the  algorithmic  solution
          being a general approach not particularized to the   considered  in  the  proposed  framework,  which
          specificities of radio resource allocation. Recently,   consists of a multi-agent DQN with one agent per
          deep Q learning has become a quite popular tool for   slice that learns the capacity to be allocated to each
          allocating radio resources to slices, as reflected by   slice  in  each  cell.  In  contrast  to  single  agent
          works  [27]-[33]  that  include  different  variants  of   solutions  like  those  of  [30],  [31],  which  jointly
          this  technique  and  address  the  problem  from    consider all the tenants when making decisions, the
          different perspectives, such as the joint allocation of   multi-agent approach has advantages such as better
          computational  resources  and  radio  resources  to   scalability  as  it  allows  easily  adding/removing
          users  in  [27],  the  allocation of  aggregate capacity   slices  in  the  scenario  simply  by  adding/removing
          per slice to multiple cells in [28], [29], the allocation   the  corresponding  agent.  Moreover,  while  some
          of resources to slices on a single cell basis in [30],   multi-agent  approaches  have  already  been
          [31], [32], or the allocation of per-cell resources to   considered  in  [28],  [29][32],  the  one  considered
          the different slices jointly considering multiple cells   here  has  the  advantage  that  an  agent  learns  the
          in [33]. Finally, other works have proposed the use   policy for assigning capacity to be provided to the
          of  traffic  forecasting  for  cross-slice  resource   slice  in  each  cell,  in  contrast  to  [32],  which
          allocation, applying techniques such as LSTM neural   considered the capacity in a single cell, or [28], [29],
          networks [34], deep convolutional neural networks    which provided the aggregated capacity over all the
          [35], Generative Adversarial Networks (GANs) [36],   cells.
          or deep neural networks [37].
                                                               3.    ML-ENABLED CROSS-SLICE
          This  paper  introduces  several  novelties  with
          respect  to  previous  works.  First  of  all,  this  paper   MANAGEMENT FRAMEWORK
          presents  a  functional  framework  aligned  with    3.1  O-RAN  framework  for          ML-assisted
          current  3GPP  and  O-RAN  specifications  for             solutions
          implementing    ML-assisted   cross-slice   radio
          resource  optimization  and  particularizes  it  to  a   As part of the specification of  new interfaces  and
          specific  algorithmic  solution  coming  from  our   functionality for an open and intelligent RAN, the O-
          previous work [33]. Instead, the above-mentioned     RAN  Alliance  is  working  on  the  definition  of  a
          works have put the focus on algorithm development    framework  for  the  deployment  of  ML-assisted
          but  without  going  into  detail  of  the  mapping  on   solutions within the RAN (i.e. solutions that rely on
          existing architectures from standardization bodies.   the use of ML models such as supervised learning,
          For  example,  some  works  just  consider  a  slicing   reinforcement learning, etc.) [38].
          controller  (e.g.  [23])  or  a  network  slicing  module   A  representation  of  the  overall  RAN  functional
          (e.g. [28], [29]) but without providing details of how   architecture being defined by O-RAN is illustrated
          this  would  be  mapped  on  practical  architectures.   in Fig. 1 [39]. This constitutes a disaggregated RAN,
          Only  in  [24]  an  architectural  framework  for  slice   compliant  with  3GPP  specifications,  where  the
          management and orchestration that is aligned with    radio protocol stack is split and distributed between
          3GPP is presented, but  without providing specific   different RAN nodes. In particular, the O-RAN Radio
          details on the algorithm implementation.             Unit (O-RU) hosts the RF processing and the lower
                                                               part  of  the  PHY  layer  functionality  (e.g.  i/FFT





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