Where’s that
fu*#}ng Wi-Fi ?!?
EFFICIENT Wi-Fi
deployments
The basics
Just some common sense rules
put together in a nice set of
colorful slides
Eduard Garcia-Villegas
Dept. of Network Engineering
eduardg@entel.upc.edu
Contents

EFFICIENT Wi-Fi deployments
o Big vs. small
o Analyze requirements
o #STAs and #Needed radios
o Available channels
o Reuse factor
o Dimensioning cells
o Optimization
by Podere Casanova
Efficient Wi-Fi deployments
2
Wi-Fi deployments: intro

In the era of ubiquitous Internet…

Wireless internet access can be a traumatic
experience due to
o Many concurrent users (dense scenarios)
o Coexistence (older/slower devices, other
technologies sharing the band, etc.)
o …
o POOR DESIGN
Efficient Wi-Fi deployments
3
Wi-Fi deployments: big vs. small (1)

Coverage-driven design
o In the past: maximize cell size && minimize costs
• Optimize AP location and increase cell size less APs
needed (lower cost)
• Problems:
– more devices per AP (lower per STA throughput)
» Reduced efficiency due to higher collision probability
5 STAs x AP
<2 STAs x AP
VS.
= AP
= STA
Efficient Wi-Fi deployments
4
Wi-Fi deployments: big vs. small (2)

Coverage-driven design
o In the past: maximize cell size && minimize costs
• Optimize AP location and increase cell size less APs
needed (lower cost)
• Problems:
– Longer distances AP  STA mean worse signal quality and,
hence, more robust (slower) PHY rates are used
» Capacity of the whole cell is reduced
» Longer tx time  more power consumed and more collisions
LAME!
= AP
= STA
Efficient Wi-Fi deployments
5
Wi-Fi deployments: big vs. small (3)

Coverage-driven design
o In the past: maximize cell size && minimize costs
• Optimize AP location and increase cell size less APs
needed (lower cost)
• Problems:
– More hidden nodes  more collisions
– Power mismatch: AP (high tx power) and STA (low tx power)
» STA can hear the AP, but the AP can't hear the STA
» If you want a big cell, increase the antenna gain, not the tx power!
HIDDEN NODES!
CAN’T REACH ITS AP!
= AP
= STA
Efficient Wi-Fi deployments
6
Wi-Fi deployments: big vs. small (4)

Coverage-driven design
o In the past: maximize cell size && minimize costs
• Optimize AP location and increase cell size less APs
needed (lower cost)
• It has problems in present (dense) deployments.

Other key aspects
o KPI requirements
o Client and AP capabilities
• Are modern ≥ 11n capable (how many antennas)?
Coexistence with 11a/b/g? Dual band?
o Propagation phenomena
• Outdoor/indoor? APs mounted on ceiling, walls or floor?
o User density
Efficient Wi-Fi deployments
7
ANALYZE REQUIREMENTS
Per user
#STAs per
RADIO
AVAILABLE
CHANNELS
Total
#RADIOS
NEEDED
Efficient Wi-Fi deployments
REUSE
FACTOR
DIMENSION
CELLS
The basics
Analyze requirements
OPTIMIZE/
TROUBLESHOOT
Wi-Fi deployments: requirements

The first thing is to identify key performance indicators (KPI)
o Minimum bandwidth required to satisfy supported
applications
o Maximum latency tolerated
o Expected Min-Avg-Max number of active devices

Examples (per-user requirements):
o School
o Convention center
(1500 att.)
• BW: <3Mbps
• BW: <1 Mbps
• Delay tolerance: low
• Delay tolerance: Medium
• Users: “educated guess”
(video
streaming; desktop/file sharing)
(video streaming; intranet login)
• Users: Min-Avg-Max =
up to 30 per classroom
Efficient Wi-Fi deployments
(web browsing;
e-mail)
– 70% will connect Wi-Fi device
– 50% simultaneously
– 1500 x 0.70 x 0.5 = 525
9
Wi-Fi deployments: #STAs and radios

Capacity-driven design (rule of thumb)
o Example 1 school (<3Mbps x 30 users per classroom):
• 20 STAs per AP  each classroom served by two radios (two
APs or one dual band AP)
– Assume homogeneous (IT-controlled) 11n 2x2 devices
– Good signal quality (high rates available)  STAs achieve
~80Mbps of net throughput (isolated)
– Allow future growth: AP utilization ≤ 75% 
75/(100*3Mbps/80Mbps) = 20 STAs per AP
o Example 2 convention center (<1Mbps x 525 users)
• 32 STAs per AP  525/32 = 16 – 17 radios
– Assume heterogeneous (BYOD) devices
– Diverse signal quality  STAs achieve ~40Mbps of net
throughput
– AP utilization ≤ 80%  80/(100*1Mbps/40Mbps) = 32 STAs/AP
Efficient Wi-Fi deployments: the basics
10
ANALYZE REQUIREMENTS
Per user
#STAs per
RADIO
AVAILABLE
CHANNELS
Total
#RADIOS
NEEDED
Efficient Wi-Fi deployments
REUSE
FACTOR
DIMENSION
CELLS
The basics
Available channels
OPTIMIZE/
TROUBLESHOOT
Wi-Fi deployments: channels (1)

Capacity limited by the scarcity of available spectrum
o 2.4GHz ISM band
• Only three non-overlapping channels (1,6,11)
• Four (almost) non-overlapping channels (1,5,9,13)
 where available
Ch1
Ch11
Ch1
Ch1
Ch11
Baseline capacity
Ch5
Ch11
Ch6
Ch6
Three channel scheme:
Baseline x3.05
Three channel scheme:
Baseline x3
Efficient Wi-Fi deployments: the basics
Ch13
Ch9
Four channel scheme:
Baseline x3.9
12
Wi-Fi deployments: channels (2)

Capacity limited by the scarcity of available spectrum
o 2.4GHz ISM band
• Only three non-overlapping channels (1,6,11)
• Four (almost) non-overlapping channels (1,5,9,13)  where available
– Not available in all regulatory domains (e.g. North Americas)
– Many devices default to Americas config.  will see coverage
gaps in the areas served by APs in Ch13.
• Highly congested: coexistence with WPANs, cordless phones,
baby monitors, microwave ovens…
o 5GHz ISM band
• 15-21 non overlapping channels in different sub bands
• Highly variable from one regulatory domain to another
– Some channels only for indoor use, others require DFS
– Different tx power limits …
Efficient Wi-Fi deployments: the basics
13
ANALYZE REQUIREMENTS
Per user
#STAs per
RADIO
AVAILABLE
CHANNELS
Total
#RADIOS
NEEDED
Efficient Wi-Fi deployments
REUSE
FACTOR
DIMENSION
CELLS
The basics
Reuse Factor
OPTIMIZE/
TROUBLESHOOT
Wi-Fi deployments: reuse factor
#Radios Needed

Reuse Factor
=
Available Channels
o If Reuse Factor ≤ 1  LUCKY YOU!
o Otherwise, each channel is shared among Reuse
Factor APs  INTERFERENCE!
• Minimize interference by.
– Carefully dimensioning cells
– Smart channel management
Efficient Wi-Fi deployments: the basics
15
ANALYZE REQUIREMENTS
Per user
#STAs per
RADIO
AVAILABLE
CHANNELS
Total
#RADIOS
NEEDED
Efficient Wi-Fi deployments
REUSE
FACTOR
DIMENSION
CELLS
The basics
Dimension the cell
OPTIMIZE/
TROUBLESHOOT
Wi-Fi deployments: dimension cells (1)

What is the cell radius?
o Max distance at which frames
can be decoded
• Pt is tx power
VERY FAST
– Decreases with MCS (to avoid distortion)
• Sr is receiver sensitivity
R1
– Increases with MCS
– Rr reception range
𝑷𝒕
𝑷𝒓 ≈ 𝜶 ⟶ 𝑹𝒓 ≈
𝒅
𝑷𝒕
𝑺𝒓
𝟏
R2
Rn
𝜶
SLOW
– d is the distance tx  rx
– α is the path loss exponent
o Different radius depending on targeted
MCS
Efficient Wi-Fi deployments: the basics
17
Wi-Fi deployments: dimension cells (2)

How to set cell radius for Wi-Fi small cells?
o Reduce AP’s tx power
• Reduces interference over
other cells
• Avoids AP/STA power
mismatch
• Reduces suitable rates
NOT SO
FAST
SLOW
Efficient Wi-Fi deployments: the basics
18
Wi-Fi deployments: dimension cells (3)

How to set cell radius for Wi-Fi small cells?
o Reduce AP’s tx power
• Reduces interference over
other cells
• Avoids AP/STA power
mismatch
• Reduces suitable rates
NOT SO
FAST
o Increase min tx rate of the cell
• Reduces performance anomaly
and allows higher average rate
OUT!
– Avoid “sticky” STAs
• Possible unsupported devices
– Accept, at least, 802.11b@11Mbps?
Efficient Wi-Fi deployments: the basics
19
Wi-Fi deployments: dimension cells (4)

BUT…interference goes beyond the cell edge
o Carrier Sense Range (Rc)
• Max distance at which frame
preamble can be detected and,
hence, prevent concurrent
transmissions in the same channel.
– Only 3dB SNR is enough! (>200m
outdoors)
– Behavior improved in IEEE
802.11ax
o Beyond Carrier Sense Range
• Transmitted frames are just noise
Efficient Wi-Fi deployments: the basics
LEAVE ME
ALONE!
20
Wi-Fi deployments: dimension cells (5)
Coverage strategy for maximal densification
o Reduce reuse distance
• Low gain directional antennas
Ch1
reduced
reuse distance
Ch1
Ch1
Ch1
120º
vs.
60º
• AP placement
– Overhead: AP installed on the ceiling/lamp posts facing down
reuse
coverage

– Side: AP installed on walls/pillars
– Floor: under floor/under seat (stadiums or auditoriums)
– Even consider mounting APs behind walls/obstacles and avoid LoS
(enriches multipath diversity leveraged by MIMO)
Efficient Wi-Fi deployments: the basics
21
ANALYZE REQUIREMENTS
Per user
#STAs per
RADIO
AVAILABLE
CHANNELS
Total
#RADIOS
NEEDED
Efficient Wi-Fi deployments
REUSE
FACTOR
DIMENSION
CELLS
The basics
Finishing touches
OPTIMIZE/
TROUBLESHOOT
Wi-Fi deployments: channel plan (1)
In your
dreams
Ch11
Ch6
Reality(t)
Ch11
Ch6
Ch1
Ch11
Ch1
Ch11
Ch6
Ch1

Dynamic and unpredictable spectrum utilization
o License-free bands!

Intelligent channel assignments are required
Efficient Wi-Fi deployments: the basics
23
Wi-Fi deployments: channel plan (2)

Automatic and dynamic channel assignments aimed at
reducing interference  maximizing performance
o APs gather information of the environment
• Number of APs detected
• Power received from neighboring APs
• Portion of time the channel was reported busy/idle by CCA
Ch. X is free!
NO INTERFERENCE!
Ch. X is free!
Efficient Wi-Fi deployments: the basics
24
Wi-Fi deployments: channel plan (3)

Automatic and dynamic channel assignments aimed at
reducing interference  maximizing performance
o APs gather information of the environment
• Number of APs detected
• Power received from neighboring APs
• Portion of time the channel was reported busy/idle by CCA
o Ideally, client STAs too (and report via IEEE 802.11k)
Ch. X is free!
Ch. X is free!
Efficient Wi-Fi deployments: the basics
25
Wi-Fi deployments: channel plan (4)

Automatic and dynamic channel assignments aimed at
reducing interference  maximizing performance
o APs (ideally, STAs too) gather information of the environment
• Number of APs detected
• Power received from neighboring APs
• Portion of time the channel was reported busy/idle by CCA
o Distributed approach (autonomous APs)
• Each AP periodically (and asynchronously) scans the medium and
chooses the least congested channel  local optimum
• Alternatively, APs collaborate (exchange information) to produce
better decisions
o Centralized approach (controller-based)
• APs send periodic reports to a controller
– Knowing the whole picture and having more resources (i.e. CPU,
memory, etc.) controller runs a sophisticated optimization algorithm 
global optimum
Efficient Wi-Fi deployments: the basics
26
Wi-Fi deployments: channel plan (5)

Other considerations
o Partially overlapping channels
• Chaotic environments (many rogue/unmanaged APs in random
channels): take the most of the spectrum by allowing the whole
channel set (not only non-overlapping)
o Channel bonding
• 40MHz or 80MHz channels provide higher rates but require more
free spectrum  not recommended in dense scenarios
o Single Channel Architecture (SCA), aka Channel Blanket
• All APs use the same channel and the same (virtual) BSSID so that
all STAs “see” one single AP
– Seamless handover: controller
decides AP delivering DL traffic
– Larger collision domain
(although DL is scheduled by
controller)
© by Extricom
Efficient Wi-Fi deployments: the basics
27
Wi-Fi deployments: load balancing (1)

Wi-Fi users are quasi-static and tend to concentrate in space
& time  hot spots
o Clients (i.e. traffic) unevenly distributed among APs
• Some APs (channels) congested and some others underutilized
o Load Balancing techniques could increase ability to satisfy QoS
requirements
A
B
C
D
E
F
• Load Balancing techniques widely
used in cellular networks
• Take advantage of overlapping areas
between neighboring cells
– Clients can be served by several BSs
– System decides the best BS for a
client depending on BSs’ loads
• Not directly applicable to Wi-Fi WLANs
– Clients decide association and
roaming, not the network
Efficient Wi-Fi deployments: the basics
28
Wi-Fi deployments: load balancing (2)

Load balancing with client-driven association in WLANs
o Typically, client STAs decide best AP based on RSSI
measurements (i.e. strongest Beacon or Probe Response Frame)
• Uneven distribution of users  uneven distribution of load
o Some APs broadcast load information (BSS Load element) and
some clients do care about it
o Network-oriented client-driven load balancing
• Band steering: encourage utilization of the 5GHz band
– If AP or controller detect a STA sending Probe Requests in the two bands
 do not send responses through 2.4GHz radios, only through 5GHz
• Disassociation/blacklisting
– Network decides STA’s best AP  the rest of APs ignore that STA
requests (if already associated, current AP sends Disassociation frame)
• Cell Breathing: adapt size of the cell
– Congested APs reduce tx power of Beacons and Probe Responses 
underutilized APs do the opposite
Efficient Wi-Fi deployments: the basics
29
Wi-Fi deployments: load balancing (3)

Example of cell breathing
o Reduce power of Beacons and Probe Responses
• do not reduce power of data frames since this will reduce suitable
rates and increase error rate
Cell A
Cell B
3
1
2
4
Efficient Wi-Fi deployments: the basics
30
Wi-Fi deployments: load balancing (3)

Example of cell breathing
o Reduce power of Beacons and Probe Responses
• do not reduce power of data frames since this will reduce suitable
rates and increase error rate
Cell A
Cell B
3
1
2
4
Efficient Wi-Fi deployments: the basics
31
Wi-Fi deployments: The End

Don’t forget the wires!
o Data/power wires to APs
• If not…multihop or mesh-based
wireless distribution system
o Uplink pipe
• Imagine all this headache for just
a DSL WAN connection…
Efficient Wi-Fi deployments: the basics
32
Some references (1)


Load balancing
o
Garcia-Villegas, E.; Vidal, R.; Paradells, J. (2006, June). “Load Balancing in WLANs through
IEEE 802.11k Mechanisms,” in 11th IEEE Symposium on Computers and Communications,
ISCC 2006.
o
Garcia-Villegas, E.; Vidal, R.; Paradells, J. (2008, July). “Cooperative Load Balancing in IEEE
802.11 Networks with Cell Breathing,” in 13th IEEE Symposium on Computers and
Communications, ISCC 2008.
o
Garcia-Villegas, E.; Ferrer, JL.; Lopez-Aguilera, E; Vidal, R.; Paradells, J. (2009). “Clientdriven load balancing through association control in IEEE 802.11 WLANs”. European
Transactions on Telecommunications, ETT vol. 20, no. 5, pp. 494-507. John Wiley & Sons.
Sensitivity control
o
Afaqui, MS.; Garcia-Villegas, E.; Lopez-Aguilera, E.; Smith, G.; Camps-Mur, D. (2015)
“Evaluation of Dynamic Sensitivity Control Algorithm for IEEE 802.11ax,” IEEE Wireless
Communications and Networking Conference, WCNC 2015, pp. 1072-1077
o
Afaqui, MS.; Garcia-Villegas, E.; Lopez-Aguilera, E.; Camps-Mur, D. (2016) “Dynamic
Sensitivity Control Algorithm leveraging adaptive RTS/CTS for IEEE 802.11ax,” in IEEE
Wireless Communications and Networking Conference, WCNC 2016
o
Afaqui, MS.; Garcia-Villegas, E.; Lopez-Aguilera, E.; Camps-Mur, D. (2016) “Dynamic
Sensitivity Control of Access Points for IEEE 802.11ax”, in IEEE International Conference on
Communications, ICC’16
Efficient Wi-Fi deployments: the basics
33
Some references (2)

Channel management
o
Garcia-Villegas, E.; Vidal, R.; Paradells, J. (2009). “Frequency assignments in IEEE 802.11
WLANs with efficient spectrum sharing”. Wireless Communications and Mobile Computing,
WCMC vol. 9, no. 8, pp. 1125-1140. John Wiley & Sons
o
Mengual, E.; Garcia-Villegas, E.; Vidal, R. (2013, September). “Channel management in a
campus-wide WLAN with partially overlapping channels,” in The 24th IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2013
o
Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2011, December). “The
Impact of Channel Bonding on 802.11n Network Management,” in 7th International
Conference on emerging Networking EXperiments and Technologies, CoNEXT’11
o
Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2014). “Intelligent Channel
Bonding in 802.11n WLANs,” IEEE Transactions on Mobile Computing, vol. 13, no. 6, pp.
1242-1255
o
Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2013, June). “Joint Rate
and Channel Width Adaptation for 802.11 MIMO Wireless Networks,” in IEEE Conf. on
Sensing, Communication, and Networking, Secon’13, pp. 167-175 (Nominee for the Best
Paper Award)
o
Deek, L.; Garcia-Villegas, E.; Belding, E.; Lee, S-J.; Almeroth, K. (2015). “A practical
framework for 802.11 MIMO rate adaptation,” Computer Networks, vol. 83, pp. 332-348
Efficient Wi-Fi deployments: the basics
34
Course offered at:
EETAC - UPC
Master's degree in Applied Telecommunications
and Engineering Management
IoT & Ubiquitous IP