The document discusses planning a long-reach passive optical network (LR-PON) for sparsely populated rural areas to address the digital divide. Key challenges in rural areas include low customer density, large distances between premises, and long fiber lengths per user. The proposed approach uses an agglomerative clustering algorithm to group user premises into capacitated clusters served by optical splitters. This aims to maximize splitter utilization and minimize total fiber cable length required for deployment. Splitter size is reduced for clusters farther from the hub to account for increased optical losses over longer distances. The algorithm places housings for larger splitters in denser areas and builds the network out from these centers.

1. Addressing the Digital Divide:
LR-PON Planning for Sparsely
Populated Areas
Saptadeep
Pal
Cezary
Zukowski
Avishek
Nag
David
B.
Payne
Marco
Ruffini
1

2. Background and Overview
2
§ 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.

3. 3
Background and Overview

4. Suggested Approaches towards Rural FTTH Network
4
§ 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

5. Motivation
5
§ 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

6. Rural LR-PON Planning
6
§ 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.

7. LR-PON Split vs ODN Reach
7
§ 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.

8. Agglomerative Clustering Algorithm
8
§ 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

9. 9
• 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.

10. Minimization of Cable Length
10
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)

11. 11
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
maps
while
considering
the
cable
deployment.
Cable
Length
MinimizaKon
HeurisKc

12. Cable Length Minimization Heuristic
12
Streets
in
red
and
ONUs
in
blue
bots

13. 13
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

14. 14
The
street
segments
adjoining
the
spliRer
are
joined
to
the
spliRer.
Cable Length Minimization Heuristic

15. 15
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

16. 16
Layout
aIer
elimina,ng
the
loops.
Cable Length Minimization Heuristic

17. 17
Final
Layout
Cable Length Minimization Heuristic

18. Test Configuration & Results
18
Major
SpliRer
Minor
SpliRer
Scenario1 S10max
=32,
R10max
=1km S11max
=
16,
R11max
=
12km
Scenario2 S10max
=32,
R10max
=2km
S11max
=16,
R11max
=11km

19. Sample Statistics of Cable length Minimization
19
§ 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.

20. 20
Thank
You!