1. 11
System
Design
Lessons
Learned
from
Muni
Wi-­‐Fi
©
Ted
Boone.
All
Rights
Reserved
Page
1
Intro
Conjecture
and
rumor
about
earlier
large-­‐scale,
municipal
Wi-­‐Fi
networks
abound.
Here
are
some
general
design
rules-­‐of-­‐thumb
backed
by
data
from
some
of
those
largest
municipal
deployments.
Engineering
1. Unlicensed
=
Noisy
While
the
dollar
cost
of
unlicensed
spectrum
is
attractive,
the
noise
in
those
bands
has
to
be
properly
accounted
for
during
system
design.
Here’s
a
chart
of
average
2.4
GHz
quiescent
noise
across
a
few
thousand
Wi-­‐Fi
access
points
in
different
urban
areas.
The
average
noise
floor
was
-­‐94dbm.
And
the
90th
percentile
noise
floor
was
-­‐88
dBm.
That's
a
useful
number
for
design
purposes.
Compared
with
the
thermal
noise
floor
of
-­‐101
dBm,
this
represents
a
13
dB
penalty
against
a
similar
licensed
channel.
That's
more
than
4
times
the
range
in
free
space.
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
<-­‐100
-­‐100
-­‐99
-­‐98
-­‐97
-­‐96
-­‐95
-­‐94
-­‐93
-­‐92
-­‐91
-­‐90
-­‐89
-­‐88
-­‐87
-­‐86
-­‐85
-­‐84
-­‐83
-­‐82
-­‐81
-­‐80
-­‐79
-­‐78
-­‐77
-­‐73
-­‐72
-­‐71
%
of
sample
dBm
Noise
Floor
DistribuWon
90th
percenWle
cutoff

2. 11
System
Design
Lessons
Learned
from
Muni
Wi-­‐Fi
©
Ted
Boone.
All
Rights
Reserved
Page
2
2. Client
power
sets
density
…
and
network
cost
An
analysis
of
the
network
link
budgets
pretty
quickly
identifies
the
weak
link
in
the
chain
as
the
link
from
the
client
to
the
mesh
node.
For
a
fairly
typical
50
mW
client
(laptop
or
smartphone),
the
client
to
node
link
is
16
dB
weaker
than
the
link
down
from
the
node
to
the
client.
More than 3 times Mobile Range
Mobile Range
Mesh Node
Main Street
CPE
The Wi-Fi
mesh node
transmits a
very strong
signal
(> 1000 mW)
A typical CPE
transmits a
signal similar
to the mesh
node
(> 500 mW)
The typical
mobile
transmits a
much weaker
signal
(~50 mW)
This
means
that
the
target
client
device
generally
dictates
the
number
of
Wi-­‐Fi
nodes
required
to
cover
an
area.
And
is
thus
the
primary
driver
for
the
cost
of
the
network.
3. 4%
clients
gained
per
dB
improvement
The
observed
uplink
SNR
threshold
for
a
reliable
link
is
13
dB.
The
following
graph
demonstrates
that
a
single
dB
improvement
in
uplink
SNR
(usually
dictated
by
client
transmit
power)
moves
4%
of
clients
across
that
critical
threshold
line.
Thus
a
single
dB
improvement
in
client
TX
power
or
antenna
gain
equates
to
a
4%
improvement
in
client
coverage.

3. 11
System
Design
Lessons
Learned
from
Muni
Wi-­‐Fi
©
Ted
Boone.
All
Rights
Reserved
Page
3
4. 5
GHz
OFDM
equipment
delivers
¼
bps/Hz
in
near-­‐
or
non-­‐line-­‐of-­‐sight
A
weeklong
average
of
over
400
5GHz
CSMA/CD
OFDM
radios
with
most
devices
setup
in
near
line-­‐of-­‐
sight
shows
that
these
radios
achieve
an
average
of
¼
bps/Hz
of
TCP
throughput
5. 5
GHz
FSK
equipment
delivers
¼
bps/Hz
in
line-­‐of-­‐sight
A
weeklong
average
of
over
350
5GHz
scheduled
FSK
radios
with
most
devices
setup
in
line-­‐of-­‐sight
shows
that
these
radios
achieve
an
average
of
¼
bps/Hz
of
TCP
throughput.
0%
1%
2%
3%
4%
5%
6%
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
%
Clients
Uplink
SNR
Client
DistribuWon

4. 11
System
Design
Lessons
Learned
from
Muni
Wi-­‐Fi
©
Ted
Boone.
All
Rights
Reserved
Page
4
6. 2.4
Wi-­‐Fi
Mesh
throughput
Effective
mesh
throughput
is
strongly
dependant
on
the
number
of
hops
that
the
data
must
take
through
the
mesh.
The
chart
below
is
for
single-­‐radio
2.4
GHz
mesh
nodes.
It
is
an
composite
of
over
5000
devices.
The
mesh
throughput
rule-­‐of-­‐thumb
is
5.5
Mbps
divided
by
the
hopcount.
7. 2.4
WiFi
Mesh
equipment
is
1/5
bps/Hz
Another
rule-­‐of-­‐thumb
related
to
the
above
analysis
is
that
2.4
GHz
mesh
efficiency
is
1/5
bps/Hz
of
TCP
throughput.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0
1
2
3
4
5
6
7
8
9
TCP
Throughput
(Mbps)
Hop
Count
Single
Radio
Mesh
Throughput
Down
Up
5.5/HC

5. 11
System
Design
Lessons
Learned
from
Muni
Wi-­‐Fi
©
Ted
Boone.
All
Rights
Reserved
Page
5
8. Network
Load
Analysis
of
the
network
load
is
outlined
on
the
following
graph.
Note:
this
graph
contains
numerous
assumptions
about
household
density,
over-­‐subscription,
and
subscriber
penetration.
It
is
broken
down
into
initial
and
long-­‐term
load
curves
based
on
the
anticipated
subscriber
penetration
in
each
term.
These
curves
conform
to
an
equation
where
the
load
looks
like
a
fixed
baseline
capacity
plus
the
high-­‐
end
oversubscribed
aggregate
𝐿𝑜𝑎𝑑 =
𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝐿𝑒𝑣𝑒𝑙 ∗ (𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝐹𝑎𝑐𝑡𝑜𝑟 + 𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑𝑠 ∗ 𝑃𝑒𝑛𝑒𝑡𝑟𝑎𝑡𝑖𝑜𝑛)
𝑂𝑣𝑒𝑟𝑠𝑢𝑏𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛
9. Optimal
network
is
Capacity
=
Load
All
the
mesh
network
capacity
numbers
and
load
curves
can
then
be
used
to
determine
the
density
of
gateways
that
are
needed
for
optimal
network
design.
The
optimal
network
design
is
achieved
where
the
capacity
is
just
equal
to
the
peak
load.
This
is
simply
the
intersection
of
the
load
and
capacity
curves.
This
intersection
can
then
be
translated
into
the
gateway
density
needed
to
support
the
expected
load.
0
5
10
15
20
25
30
35
40
45
50
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Offered
Load
(Mbps)
Range
(miles)
Offered
Load
Curve
Long-term load approaches the
oversubscription rate at the
long-term sub penetration
Initial load approaches the
oversubscription rate at the
initial sub penetration

6. 11
System
Design
Lessons
Learned
from
Muni
Wi-­‐Fi
©
Ted
Boone.
All
Rights
Reserved
Page
6
10. Backend
Systems
Should
Come
First
Management
of
a
network
with
more
than
5000
devices
is
an
enormous
task.
Attempting
this
without
a
holistic,
structured,
automated
system
will
result
in
chaos
and
management
costs
will
scale
linearly
with
network
size;
an
unsustainable
network
growth
model.
Furthermore,
if
the
network
is
deployed
in
advance
of
proper
management
systems,
performance
issues
due
to
improper
deployment
will
not
be
identified
early
enough
to
rectify
while
deployment
crews
are
in
the
field.
Ideally
the
deployment
signoff
will
tie
directly
to
performance
metrics
reported
directly
from
the
backend
systems.
11. Household
density
tends
towards
the
80/20
rule
An
analysis
of
household
density
in
several
municipalities
lends
support
to
the
80/20
rule.
That
is
that
80%
of
the
population
resides
in
20%
of
the
land
area.
These
ratios
vary
significantly
with
municipality,
but
simple
math
shows
that
the
costs
of
95%
street-­‐level
coverage
are
vastly
higher
and,
more
importantly,
vastly
less
profitable,
than
a
model
that
targets
customer
use
areas
only.
Optimally,
devices
would
be
added
on
a
device-­‐level
return-­‐on-­‐investment
basis
only.