Basic concepts to understand Wi-Fi deployments: cell dimensionin, frequency management, AP placement, load balancing, etc.

1. Eduard Garcia-Villegas
Dept. of Network Engineering
eduardg@entel.upc.edu
Just some common sense rules
put together in a nice set of
colorful slides
EFFICIENT Wi-Fi
deployments
The basics
Where’s that
fu*#}ng Wi-Fi ?!?
Download this presentation (PDF) from:
http://ocw.upc.edu/sites/default/files/materials/15016145/efficientwi-fideployments-5709.pdf

2. 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
Efficient Wi-Fi deployments 2
by Podere Casanova

3. Wi-Fi deployments: intro
Efficient Wi-Fi deployments 3
 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

4. Wi-Fi deployments: big vs. small (1)
Efficient Wi-Fi deployments 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)
• Problems:
– more devices per AP (lower per STA throughput)
» Reduced efficiency due to higher collision probability
5 STAs x AP <2 STAs x AP
= STA
VS.
= AP

5. Wi-Fi deployments: big vs. small (2)
Efficient Wi-Fi deployments 5
= STA
= AP
LAME!
 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

6. Wi-Fi deployments: big vs. small (3)
Efficient Wi-Fi deployments 6
= STA
= AP
HIDDEN NODES!
CAN’T REACH ITS AP!
 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!

7. Wi-Fi deployments: big vs. small (4)
Efficient Wi-Fi deployments 7
 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

8. Efficient Wi-Fi deployments
The basics
Analyze requirements
ANALYZE REQUIREMENTS
Per user Total
#STAs per
RADIO
#RADIOS
NEEDED
AVAILABLE
CHANNELS
REUSE
FACTOR
DIMENSION
CELLS
OPTIMIZE/
TROUBLESHOOT

9. Wi-Fi deployments: requirements
Efficient Wi-Fi deployments 9
 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
• BW: <3Mbps (video
streaming; desktop/file sharing)
• Delay tolerance: low
(video streaming; intranet login)
• Users: Min-Avg-Max =
up to 30 per classroom
o Convention center (1500 att.)
• BW: <1 Mbps (web browsing;
e-mail)
• Delay tolerance: Medium
• Users: “educated guess”
– 70% will connect Wi-Fi device
– 50% simultaneously
– 1500 x 0.70 x 0.5 = 525

10. Wi-Fi deployments: #STAs and radios
Efficient Wi-Fi deployments: the basics 10
 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

11. ANALYZE REQUIREMENTS
Per user Total
#STAs per
RADIO
#RADIOS
NEEDED
AVAILABLE
CHANNELS
REUSE
FACTOR
DIMENSION
CELLS
OPTIMIZE/
TROUBLESHOOT
Efficient Wi-Fi deployments
The basics
Available channels

12. Wi-Fi deployments: channels (1)
Efficient Wi-Fi deployments: the basics 12
 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
Baseline capacity
Three channel scheme:
Baseline x3
Ch1
Ch11
Ch6
Three channel scheme:
Baseline x3.05
Ch1
Ch11
Ch6
Ch11
Four channel scheme:
Baseline x3.9
Ch1
Ch5
Ch9
Ch13

13. Wi-Fi deployments: channels (2)
Efficient Wi-Fi deployments: the basics 13
 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 …

14. ANALYZE REQUIREMENTS
Per user Total
#STAs per
RADIO
#RADIOS
NEEDED
AVAILABLE
CHANNELS
REUSE
FACTOR
DIMENSION
CELLS
OPTIMIZE/
TROUBLESHOOT
Efficient Wi-Fi deployments
The basics
Reuse Factor

15. Wi-Fi deployments: reuse factor
Efficient Wi-Fi deployments: the basics 15
#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

16. ANALYZE REQUIREMENTS
Per user Total
#STAs per
RADIO
#RADIOS
NEEDED
AVAILABLE
CHANNELS
REUSE
FACTOR
DIMENSION
CELLS
OPTIMIZE/
TROUBLESHOOT
Efficient Wi-Fi deployments
The basics
Dimension the cell

17. Wi-Fi deployments: dimension cells (1)
Efficient Wi-Fi deployments: the basics 17
 What is the cell radius?
o Max distance at which frames
can be decoded
• Pt is tx power
– Decreases with MCS (to avoid distortion)
• Sr is receiver sensitivity
– Increases with MCS
– Rr reception range
– d is the distance tx  rx
– α is the path loss exponent
o Different radius depending on targeted
MCS
𝑷 𝒓 ≈
𝑷 𝒕
𝒅 𝜶
⟶ 𝑹 𝒓 ≈
𝑷 𝒕
𝑺 𝒓
𝟏
𝜶
VERY FAST
SLOW
R1 R2 Rn

18. Wi-Fi deployments: dimension cells (2)
Efficient Wi-Fi deployments: the basics 18
 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

19. Wi-Fi deployments: dimension cells (3)
Efficient Wi-Fi deployments: the basics 19
 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
o Increase min tx rate of the cell
• Reduces performance anomaly
and allows higher average rate
– Avoid “sticky” STAs
• Possible unsupported devices
– Accept, at least, 802.11b@11Mbps?
OUT!
NOT SO
FAST

20. Wi-Fi deployments: dimension cells (4)
Efficient Wi-Fi deployments: the basics 20
 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
LEAVE ME
ALONE!

21. Wi-Fi deployments: dimension cells (5)
Efficient Wi-Fi deployments: the basics 21
 Coverage strategy for maximal densification
o Reduce reuse distance
• Low gain directional antennas
• AP placement
– Overhead: AP installed on the ceiling/lamp posts facing down
– 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)
120º
vs.
60º
coverage
reuse
Ch1 Ch1Ch1Ch1
reduced
reuse distance

22. ANALYZE REQUIREMENTS
Per user Total
#STAs per
RADIO
#RADIOS
NEEDED
AVAILABLE
CHANNELS
REUSE
FACTOR
DIMENSION
CELLS
OPTIMIZE/
TROUBLESHOOT
Efficient Wi-Fi deployments
The basics
Finishing touches

23. Wi-Fi deployments: channel plan (1)
Efficient Wi-Fi deployments: the basics 23
Ch11
Ch11
Ch6
Ch1
Ch6
Ch11
Ch1
Ch6
Ch11
Ch1
In your
dreams
Reality(t)
 Dynamic and unpredictable spectrum utilization
o License-free bands!
 Intelligent channel assignments are required

24. Wi-Fi deployments: channel plan (2)
Efficient Wi-Fi deployments: the basics 24
NO INTERFERENCE!
 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!
Ch. X is free!

25. Wi-Fi deployments: channel plan (3)
Efficient Wi-Fi deployments: the basics 25
 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!

26. Wi-Fi deployments: channel plan (4)
Efficient Wi-Fi deployments: the basics 26
 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

27. Wi-Fi deployments: channel plan (5)
Efficient Wi-Fi deployments: the basics 27
 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

28. Wi-Fi deployments: load balancing (1)
Efficient Wi-Fi deployments: the basics 28
 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
• 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
BA C
D E F

29. Wi-Fi deployments: load balancing (2)
Efficient Wi-Fi deployments: the basics 29
 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

30. Wi-Fi deployments: load balancing (3)
Efficient Wi-Fi deployments: the basics 30
 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
1
2
3
4

31. Efficient Wi-Fi deployments: the basics 31
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
1
2
4
3

32. Wi-Fi deployments: The End
Efficient Wi-Fi deployments: the basics 32
 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…

33. 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). “Client-
driven 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

34. 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

35. EETAC - UPC
Master's degree in Applied Telecommunications
and Engineering Management
IoT & Ubiquitous IP
Course offered at: