Addressing the Digital Divide: LR-PON Planning for Sparsely

Populated Areas

Saptadeep  PalCezary  ZukowskiAvishek  Nag

David  B.  PayneMarco  Ruffini

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Background and Overview

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§ Sparse  Popula,on  :  Density  of  poten,al  customers  is  usually  very  low                                    Cost  of  Deployment  per  user  increases

§ User  premises  are  distributed  over  a  large  geographical  area  separated  by  larger  distances.

                                   Fibre  length  per  user  is  high

§  Generally,  these  areas  are  far  away  from  urban  centers                                    More  fibre  required  to  connect  to  metro/core  nodes

§ Due  to  lesser  take-­‐rate  and  increased  infrastructure  deployment  per  user,network  operators  are  reluctant  to  build  FTTH  networks  for  rural  areas.

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Background and Overview

Suggested Approaches towards Rural FTTH Network

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§ GPON  extenders  are  typically  used  to  extend  the  reach.  But  such  extenders  oIen  carry  OEO  regenerators  and  thus  cost  increases.

§ Using  Raman  Amplifier  and  SOA  at  CO

§ Using  dual-­‐wavelength  for  upstream  and  downstream

§ OFDM  over  WDM/TDM  for  beRer  transmission  over  longer            distances.  

§ 1024-­‐split  architecture  for  a  reach  of  100  km  (90  km  –Backhaul  +  10  Km  distribu,on  sec,on).  But  in  rural  areas,  ge]ng  1000  customers  within  a  span  of  10  Km  is  oIen  very  difficult

Courtesy:  Telnet

Motivation

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§ Telecom   operators   are   reluctant   to   build   fibre   networks   in   less   dense   rural  loca,ons.

§ But   recently,   discussions   on   deploying   the   fibre   network   with   the   help   of  exis,ng   infrastructure  from  power   grids,   rail  networks,  motorways  has  come  up.

§ LR-­‐PON  with  3-­‐way  spli]ng  is  favoured  solu,on  for  the  industry:

Ø Longer  range  than  GPON  or  Extended-­‐GPON.Ø Split  ra,o  is  significantly  higher  than  that  of  GPON  (1024  vs  128).Ø More  consolida,on  of  central  offices  into  huge  metro  nodes            leads  to  a  simpler  network  and  also  flexibility  for  future  extension.

§ While  60  –  70  %  of  the  network  deployment  cost  is  incurred  in  trenching  while  laying  the  fibre  cables.

Ø   In   this  work,  we  have  tried   to  minimize  the  fibre  cables   instead   of  total  fibre  length.

ADSL2  Coverage

VDSL  Coverage

Rural LR-PON Planning

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§ LR-­‐PON   layout   is     a   “lollipop  model”   that   uses   a  maximum  feeder   fibre   length   of  90   km   and   distribuKon   secKon  of  10  km   .The  maximum   number   of  ONUs   per   PON  wavelength   is  typically  up  to  1024.  

§ But,   in   sparse   rural   areas   it  will   be   necessary   to   connect   to  customers  at  different  points  down  the  feeder  route.  

§ The   Op,cal-­‐Distribu,on-­‐Network   (ODN)   reach   needs   to   be  extended   and   alterna,ve   configura,ons   are   considered   with  longer  distribuKon  secKon  and  shorter  feeder.  

§ In  such  a  case,  where  the  fibre  losses   in  ODN  secKon  will  be  more,  the  number  of  splits  needs  to  be  reduced.

LR-PON Split vs ODN Reach

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§ The  figure  shows  how  the  LR-­‐PON  split  ra,o  declines  with  the  increase  in  the  ODN  reach.

§ In   this   work,   we   have   used   this   knowledge   to   plan   the  network   and   maximize   resource   usage   in   sparsely  populated  areas.

§ So,   for  a  strategic  deployment  in   rural  areas,  a  clustering  algorithm  is  required   to  decide   the  number  of  ONUs  per  spliRer.

Agglomerative Clustering Algorithm

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§ The  algorithm  takes  the  loca,on  of  the  user  premises  (ONUs)  and  groups  them  into  capacitated  clusters  to  achieve  maximum  u,liza,on  of  the  spliRer.  The  algorithm  runs  in  stages

§ The  algorithm  first  tries  to  place  spliRers  with  the  largest  split  (32-­‐way  split).  The  largest  split  has  the  least  span.  This  largest  spliRer  posi,on  will  then  be  the  loca,on  of  the  cabinet  housing

§ Subsequently,   these  housing  posi,ons  will  be  then  used  to  host  other   smaller   size  spliRers   to  connect  the  users   who   could   not   be   reached   due   to   limita,ons   in   the   reach   of   larger   spliRers,   thus   leading   to  agglomera,on  of  more  than  one  type  of  spliRers  at  a  certain  geographical  loca,on

§ The  algorithm  looks  to  place  the  housings  in  the  denser  areas  and  build  the  network  around  these  centers

§ In  each  itera,on,   the  algorithm  tries  to  maximize  the  u,liza,on  of  each  of  the  spliRers,   thus  searching   for  the  op,mum  loca,on  of  placing  the  housings

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• The  Red  links  are  links  from  the  32-­‐way  split

• The   Yellow   ones   are   links   from   the   16-­‐way  spliRer

• The  orange  ones  are  from  the  spliRers  with   less  than  16-­‐way  spli]ng

• It  can  be  clearly  no,ced  that  most  of  the  cabinet  housings  with   larger   spliRers  are   located   in   the  denser  regions.

Minimization of Cable Length

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We   then   approach   the   cable   length  minimizaKon   problem   using   an   ILP   and   a   heurisKc.   Cable   deployment  follows  the  street   layout  (taken   from   the  open  source  open  maps  database).  Close  to  a  user  premises,   a  final  drop  cable  is  branched  off  the  public  roads  to  connect  the  individual  user.  We  call  this  branching  point  the  final  drop  point.  The  link  from  there  to  the  user  premises  is  normally  achieved  with  an  aerial  cable.

Representa,on  of  main  roads,  ONUs  and  Delivery  points  (white  circles  with  black  dot  on  streets)

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The  informa,on  about  the  spliRer  posi,on  and  the  ONUs  to  be  served  by  the  spliRer  is  provided  by  the  clustering  algorithm  and  forwarded  as  input  to  the  heuris,c.  The  heuris,c  also  considers  the  street  mapswhile  considering  the  cable  deployment.

Cable  Length  MinimizaKon  HeurisKc

Cable Length Minimization Heuristic

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Streets  in  red  and  ONUs  in  blue  bots

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The  spliRer  posi,on   is  determined  by  the  agglomera,ve  clustering   algorithm.  Firstly,  our  heuris,c  finds  the  nearest  point  on  a  main  street  for  each  of  the  ONUs  (i.e.,   the  final  drop  points)  similar  to  the  ILP  model  and  the  drop  points  on  same  street  are  joined  together.

Cable Length Minimization Heuristic

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The  street  segments  adjoining  the  spliRer  are  joined  to  the  spliRer.

Cable Length Minimization Heuristic

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Now  the  connected  segments  are  recursively  connected  to  the  other  segments  which  are  required  to  be  connected.  Note  that  in  this  case,  one  segment  might  be  connected  to  the  more  than  one  already  connected  segment,  we  only  consider  the  shortest  connec,on.

Cable Length Minimization Heuristic

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Layout  aIer  elimina,ng  the  loops.  

Cable Length Minimization Heuristic

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Final  Layout

Cable Length Minimization Heuristic

Test Configuration & Results

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Major  SpliRer   Minor  SpliRer

Scenario1 S10max  =32,  R10max  =1km S11max  =  16,  R11max  =  12kmScenario2 S10max  =32,  R10max  =2km   S11max  =16,  R11max  =11km

Sample Statistics of Cable length Minimization

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§ Though  Dijsktra  Algorithm  results  in  about  15%  lesser  total  fibre  required,  our  proposed  algorithm  significantly  decreases  the  amount  of  total  fibre  cable  used  by  about  24%  and  30%  respec,vely.  

§ The  proposed  heuris,c  is  approximately    6  –  ,mes  faster  than  the  ILP  while  the  heuris,c  performance  as  good  as  that  of  ILP’s  with  approximately  5%  varia,on  in  the  results.

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Thank  You!