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GDS International - Next - Generation - Telecommunications - Summit - Europe - 8
 

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The Financial Benefits of RAN Aware Policy Management & Video Optimisation

The Financial Benefits of RAN Aware Policy Management & Video Optimisation

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    GDS International - Next - Generation - Telecommunications - Summit - Europe - 8 GDS International - Next - Generation - Telecommunications - Summit - Europe - 8 Document Transcript

    •   Financial  Benefits  of  RAN  Aware  Policy  Management  and   Video  Optimization    It  is  well  known  that  mobile  broadband  data  is  growing  very  rapidly  and  that  it  poses  several  challenges  to  operators  to  deal  with.  Operators  are  challenged  with  keeping  up  with  the  traffic  growth,  most  of  it  from  video  applications,  and  with  implementing  a  sustainable  revenue  capture  model  that  supports  network  infrastructure  investment.    Typical  questions  pertaining  to  the  management  and  optimization  of  mobile  broadband  networks  relate  to  real  time  visibility,  either  directly  or  indirectly:   1. What  is  the  full  capacity  of  the  network  per  cell  area?  Relative  to  this  full   capacity  how  is  the  network  operating?   2. Across  the  different  resource  pools  (radio,  signaling  and  bandwidth)  how  is   the  network  loaded?   3. Can  you  get  more  capacity  out  of  a  network  by  evening  out  the  load?   4. Can  you  safely  offer  premium  services  in  real-­‐time  and  rely  on  the  network  to   come  through  on  the  promise?  If  so,  can  this  be  a  driver  for  incremental   revenue  growth?   5. What  applications  are  subscribers  using  and  which  of  these  pose  the  biggest   challenge  to  network?   6. Based  on  information  about  which  applications  subscribers  are  using,  where   they  are  using  them  and  when,  is  it  possible  to  devise  new  services  that  can   help  business  growth  and  customer  satisfaction?   7. Is  it  possible  to  focus  video  compression  on  cells  with  congestion  issues?   8. Can  video  optimization  focus  on  users  in  areas  with  certain  radio  conditions?   9. Can  video  optimization  differentiate  between  different  types  of  users  and   handsets?    The  CommProve  BCN  RAN  solution  is  aimed  at  helping  operators  deal  with  the  complexities  of  managing  and  optimizing  mobile  broadband  data  networks.  Such  networks  are  loaded  very  unevenly  as  users  move  around  and  change  their  behaviors  and  usage  patterns.  This  dynamic  environment  calls  for  an  equally  dynamic  and  responsive  real  time  approach  to  optimization  and  revenue  capture.  Currently,  networks  are  managed  with  only  limited  insight  into  quality  of  experience  (QoE)  and  actual  network  traffic  levels  relative  to  capacity.  This  makes  it  difficult  if  not  impossible  to  properly  handle  radio  access  network  (RAN)  congestion  and  it  does  not  allow  for  introduction  of  real-­‐time  premium  services.  Policy  control  solutions  aim  to  address  these  shortcomings.    With  traditional  policy  control  solutions  there  is  an  interplay  between  three  elements:  1)  deep  packet  inspection  and  policy  enforcement,  2)  PCRF  or  the  rules  function  and  3)  billing  systems  with  subscriber  specific  information  and  plans.  Traditionally,  deep  packet  inspection  is  done  in  the  core  of  the  network  (Gn  or  Gi  interface)  from  which  information  about  who  the  users  are  and  what  they  are  doing     1    
    •  is  gathered.  This  information  is  sometimes  augmented  with  information  from  the  Gx  interface  (Radius  protocol)  adding  a  level  of  location  awareness  but  not  in  real-­‐time.  This  information  is  fed  to  the  rules  function  (PCRF).  The  rules  function  also  gets  per  subscriber  information  in  terms  of  pre-­‐paid  plans  etc.  from  the  billing  system.  Based  on  this,  the  PCRF  determines  which  rules  on  a  per  subscriber  basis  should  be  enforced.  The  enforcement  is  done  by  the  DPI  solution.    Similarly,  solutions  are  deployed  at  the  Gi  or  Gn  interface  focusing  in  on  video  optimization  as  the  majority  of  the  mobile  broadband  growth  comes  from  a  variety  of  video  applications.  For  such  solutions  optimization,  both  in  terms  of  improved  video  quality  and  bandwidth  compression,  works  well  across  all  users  and  geographical  areas,  but  if  optimization  needs  to  be  done  for  specific  users,  or  by  differentiating  between  cell  areas  depending  on  their  congestion  levels,  for  example,  there  are  certain  shortcomings.    These  concepts  only  work  well  when  users  aren’t  mobile  and  as  long  as  they  stay  close  to  base  stations.      What  is  clearly  lacking  is  a  set  of  subscriber  specific  information.  The  policy  manager  or  the  video  optimization  solution  knows  nothing  about  handset  received  power  levels  or  interference  power  levels.  It’s  only  possible  to  view  users  who  can  get  on  the  network,  and  to  manage  bandwidth  resources  in  the  core  of  the  network  but  there  is  no  insight  into  signaling  resources  or  radio  resources  and  last  but  not  least  there  is  no  real-­‐time  information  about  the  location  of  each  subscriber.    Therefore,  policies  are  enforced  with  no  real-­‐time  visibility  to  subscriber  location  or  movement,  and  with  no  visibility  to  the  subscriber  impact  on  other  subscribers  in  terms  of  network  interference,  leading  in  many  cases  to  throttling  of  the  wrong  user.  As  an  example,  if  one  user  is  close  to  a  base  station  but  consuming  a  lot  of  bandwidth  the  policy  manager  is  likely  to  make  this  individual  a  candidate  for  throttling.  If  at  the  same  time  another  user  is  far  from  the  base  station,  even  if  he  is  consuming  less  bandwidth,  he  may  be  a  much  better  candidate  for  throttling  due  to  introduction  of  service  impacting  network  interference  from  the  higher  power  levels  in  connection  with  the  communication.  Additionally,  when  policies  are  enforced  or  video  is  optimized  it  is  done  in  a  push  like  fashion  with  no  feedback  loop  from  the  RAN  with  QoE  information  about  what  the  policies  did  to  the  satisfaction  of  the  subscribers.  Finally,  with  no  information  about  service  conditions  at  the  location  of  each  of  the  subscribers,  operators  are  not  able  to  offer  real-­‐time  premium  services  as  it’s  just  too  risky.    BCN  RAN  was  introduced  to  address  these  issues  head-­‐on.  The  BCN  RAN  solution  collects  and  processes  in  real-­‐time,  measurements  from  different  interfaces  of  the  RAN.  DPI  measurements  and  RAN  quality  measurements  are  both  collected  24x7  network-­‐wide.  Interface  monitoring  includes  Iub  and  IuPS  interfaces.  DPI  measurements  provide  information  concerning  application  and  network  level  delays,  retransmissions,  peak  and  average  throughput,  application  type  breakdown,  both  at     2    
    •  user  and  cell  level  granularity.        The  cell  level  granularity  is  essential  to  understanding  if,  how  and  where  the  load  generated  by  the  user  is  affecting  the  network  availability  for  other  users.  This  information  is  generated  leveraging  BCN  RAN’s  correlation  capabilities;  user  mobility  is  tracked  and  the  actual  cells  serving  the  user  are  saved  in  a  call  detail  record  (CDR),  along  with  all  relevant  signaling  information  (Call  Set-­‐up/Tear  down,  RAB  Assignment/Release,  Failure  Events,  Inter-­‐RAT  Handover  events).      With  the  Iub  interface  monitoring  included,  the  signaling  procedures  of  the  radio  interface  can  be  logged  (HDSPA/HSUPA  transactions,  radio  bearer  set  up/reconfigurations,  soft/hard  handovers,  transitions  between  Common  Channels  and  Dedicated  Channels)  and  correlated  with  channel  quality  measurements  (Received  Signal  Code  Power,  Ec/N0,  Transmitted  Carrier  Power  and  Received  Signal  Strength  Indicator).  The  additional  measurements  from  the  Iub  interface  allow  the  Policy  Manager  to  differentiate  between  the  users  based  on  the  effect  their  data  consumption  has  had  on  the  congested  cell  resources;  a  data  intensive  user  close  to  the  antenna  and  in  good  visibility  conditions  is  much  lighter  on  the  radio  network  than  a  stationary  lower  bandwidth  indoor  user  in  a  severe  interference  condition.  If  air  interface  resources  are  congested,  differentiating  between  these  two  types  of  users  is  vital.  Throttling  the  latter  user  is  much  more  effective  in  addressing  the  congestion.  On  the  other  hand,  if  the  bottleneck  is  located  at  the  backhaul  level,  it  is  the  first  user  who  should  be  throttled  as  he  or  she  is  consuming  more  of  the  transport  resources.  CommProve’s  BCN  RAN  is  the  only  solution  capable  of  capturing  and  making  sense  of  this  type  of  information  as  well  as  base  station  power  allocated  to  each  user,  which  in  some  cases  can  be  a  more  scarce  resource  than  bandwidth.      BCN  RAN’s  real-­‐time  correlated  CDRs  provide  the  Policy  Manager  with  all  the  information  needed  to  make  the  Policy  Control  decision  based  not  only  on  the  user  data  consumption  information  but,  most  notably,  on  how  the  user  data  consumption  is  affecting  the  network  resources.  The  Policy  Control  decision  can  be  taken  in  a  much  more  effective  way,  minimizing  the  perceived  effect  on  the  user  QoE  and  maximizing  the  utilization  of  the  network  resources.    Finally,  with  BCN  RAN  the  Policy  Manager  becomes  QoE  aware  in  the  sense  that  after  policies  are  enforced  BCN  RAN  can  tell  the  Policy  Manager  what  it  did  to  QoE  and  before  any  area  is  deemed  to  be  in  congestion  in  the  first  place  BCN  RAN  can  use  a  QoE  based  congestion  measurement  to  tell  the  Policy  Manager  that  congestion  has  occurred.  The  user  specific  QoE  information  can  also  be  used  to  enable  premium  services  in  real-­‐time  for  incremental  revenue  capture  from  mobile  broadband  data.  All  of  the  BCN  RAN  functionality  is  offered  in  a  stand-­‐alone  point  solution  or  it  can  be  implemented  as  a  software  application  on  the  CommProve  NetLedge  network  monitoring  system.       3    
    •  In  order  to  quantify  the  financial  benefits  of  BCN  RAN,  specific  network  information  is  needed.  The  financial  benefits  of  BCN  RAN  fall  into  three  categories:   1. With  BCN  RAN  it  is  possible  to  load  networks  with  more  traffic  and  as  such   defer  CAPEX  expenditures  on  network  infrastructure  expansions.  Often  cells   are  loaded  at  less  than  25%  capacity  but  operators  do  not  have  access  to  this   type  of  information  and  they  dont  have  the  tools  to  even  things  out   2. With  real-­‐time  visibility  of  QoE  for  subscribers  operators  can  better  manage   customer  satisfaction  and  subsequently  address  any  churn  issues   3. Also  based  on  real-­‐time  visibility  of  QoE,  BCN  RAN  is  an  enabler  for  offering   real-­‐time  premium  services  and  as  such  for  incremental  revenue  growth.   How  this  is  done  depends  on  what  services  the  operator  is  interested  in   providing.  Additionally  with  the  per  subscriber  insight  into  applications  used,   their  location,  their  QoE  and  how  they  load  the  network,  operators  gain   insight  into  which  services  to  offer.  This  can  also  be  used  to  estimate  the   merit  of  marketing  campaigns  by  comparing  subscriber  behavior  before   campaigns  are  launched  with  how  this  develops  in  the  hours  and  days  after   launch.     4