moe defense
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Load Distribution and Channel
Assignment in IEEE 802.11Wireless Local Area Networks
Ph.D. Dissertation DefensePresented by Mohamad Haidar
Department of Applied Science
George W. Donaghey College of Engineering and
Information Technology,University of Arkansas at Little Rock
November 9, 2007
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Presentation Outline
Introduction Wireless Local Area Networks (WLANs)
Access Points (APs) Congestion
Channel Assignment
Related Work Contributions
Problems Statements1. Congestion Problem
Proposed Solution
Problem Formulation
Algorithm
Numerical Analysis and Results
Simulations (OPNET)
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Presentation Outline (Contd)2. Channel Assignment Problem
Proposed Solution
Problem Formulation
Algorithm
Numerical Analysis and Results
Simulations (OPNET)
Dynamic Model
Scenario 1 (variable data rate)
Scenario 2 (dynamic user distribution)
Conclusion
Future Work
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Introduction
Wireless Local AreaNetworks (WLANs) Airports
Hotels Campuses
WLANs are divided into 3categories: IEEE 802.11a in the 5 GHz
band (54 Mbps) IEEE 802.11b in the 2 GHz
band (11 Mbps) IEEE 802.11g in the 2 GHz
band (54 Mbps)
Example of WLAN
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Introduction (Contd) What is Access Point (AP)
congestion? Some times referred to as Hot
Spot
CAP= (R1+ R2+..+ RN)/BW
CAP: Congestion at APR : Data rate of a user connected to the APBW: Bandwidth (11 Mbps for IEEE
802.11b)
Channel Assignment Minimize interference
To improve QoS (less delay andhigher throughput)
3 non-overlapping channels in IEEE802.11b/g (1, 6, and 11) Frequency Spectrum for IEEE 802.11b/g
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Limitation of Previous Research AP Placement
The main objective was to use a minimum number of APs foradequate coverage of the desired area. Did not account for channel assignment and/or load distribution.
Channel Assignment Based on minimizing co-channel interference.
Limited to either minimizing total interference between APs ormaximizing the sum of interference at a given AP.
When integrated and applied simultaneously with AP placement,
better results were achieved than dealing with them sequentially. User distribution was not accounted for in the channel assignment.
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(Contd) Load Balancing/Distribution
Balancing the load based on the number of active users performs poorly because the data rate of users was not
taken into consideration.
Minimizing the congestion at the most congested AP byredistributing users. Improves the load ONLY at the MCAP.
Load balanced agents installed at the APs that broadcastperiodically their load. APs are either under-loaded,balanced, or overloaded. Static user distribution and no power management. All APs involved should be equipped with the LBA software.
Cell breathing technique used to reduce the cell size to
achieve a better load distribution. Connects to the next higher RSSI: is not always the best
choice. Static user distribution. No channel assignment was considered. Interference was
not accounted for.
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Contributions of the Current
Research A new Load Balancing scheme based on Power
Management.
As long as the received power exceeds a certain
threshold, that AP is a potential for association.
Channel Assignment based on Maximizing theSIR at the users.
Users involved in the assignment of channels. Different user distributions will lead to different
channel assignment.
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(Contd)
Combining both load balancing based on powermanagement and the channel assignment basedon SIR: A Novel Scheme.
Verified the performance predicted fromoptimization versus realistic OPNET-basednetwork simulations: New contribution
Developed a realistic dynamic model approachthat accounts for variable users data rates andusers behavior: New contribution
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A New Heuristic AlgorithmInitial Channel Assignment
Users enter to network
Load Balancing based on PM
Re-Assign channels based on SIR
Sort arriving users and departing usersin ascending order in a list
Check list
Arrive Depart
Add user to list Remove user from listResults
End of list
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1st Problem
AP Congestion Problem
Degrades network throughput
Slowest station will make other stations wait longer. Unfair load distribution over the network
causes bottlenecks at hot spots.
Inefficient bandwidth utilization of the network.
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Proposed Solution
Reduce congestion at the hot spots by decrementing thepower transmitted by the Most Congested AP (MCAP) indiscrete steps until one or more users can no longerassociate with any AP or their data rate can no longer beaccommodated.
The final transmitted power of each AP is set to the bestbalance index, , achieved.
Advantages: Load is fairly distributed. Increase in data rate throughput per user. Less adjacent and co-channel interference.
2
2
( )
( * )
j
j
T
n T
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Problem Formulation
MCAP NLIP formulation
minijx
1 2max{ , ,..., }MC C C1 i M 1 j N
1
1N
ij
i
x
1
M
i ij
j
j
U x
Cj
BW
forj= 1,, M
fori= 1,,N
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Algorithm Compute Received Signal Strength Indicator (RSSI)
at each user. Generate a binary matrix that assigns 1 if a users
RSSI exceeds the threshold value or 0 otherwise.
Invoke LINGO to solve the NLIP. Identify the MCAP and compute . Decrement its transmitted power by 1 dBm. Repeat previous steps until one or more user can no
longer associate with an AP or their data rate can nolonger be accommodated. Observe the power levels at each AP and the best
users association at the best .
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Numerical Analysis and Results
User Number AP1 AP2 AP3 AP4
1 1 1 1 0
2 0 0 1 0
3 0 0 0 1
4 0 0 0 1
5 1 1 1 1
6 1 1 0 0
7 0 1 0 0
8 0 0 1 1
9 0 0 1 0
10 0 1 0 1
Receiver Sensitivity at the user is-90 dBm
Transmitted Power at each AP is
20 dbm4
1
2
User-AP candidate association
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Numerical Analysis and Results(Contd)
Service Area Map
Traffic is randomly generated between 1 Mbpsand 6 Mbps for each userUser Number Traffic (Kbps)
1 1752
2 5698
3 4265
4 1994
5 3558
6 3176
7 5319
8 1559
9 2982
10 2263
Data rate of users
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Numerical Analysis and Results
Each user is associated toone and ONLYone AP.
User Number AP1 AP2 AP3 AP4
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1
4 0 0 0 1
5 0 1 0 0
6 1 0 0 0
7 0 1 0 0
8 0 0 0 1
9 0 0 1 0
10 0 1 0 0
1
1
1
Optimal user-AP association
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Numerical Analysis and Results
(Contd)Initial Congestion factor:
(No Power Mgmt)
Congestion factor solution
according to [2]
Congestion factor with
Power Mgmt
AP1 0.6319 0.5234 0.3793
AP20.4100 0.4100 0.3617
AP3 0.2117 0.2117 0.3167
AP4 0.2026 0.3110 0.3985
81.15% 90.84% 99.31%
Congestion Factor comparison
Load is distributed fairly among APs. Final transmitted power levels at each AP is: 12 dBm, 18
dBm, 20 dBm and 17 dBm, respectively.
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Numerical Analysis and Results
(Contd) Different radii sizes
after poweradjustment
Users do NOT alwaysassociate to the closestAP.
Service area map after Power Mgmt
N i l A l i d R l
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11/09/2007 *published at IEEE Sarnoff ConferenceMay'07 20
Numerical Analysis and Results(Contd)4 APs 9 APs
16 APs
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11/09/2007 *Not published yet 21
Simulation Scenarios (OPNET)
Unbalanced Load v.s.Balanced Load
20 dBm Transmitted power
-90 dBm Receiver threshold
FTP clients and APs
are stationary
File of 50 Kbytes uploaded
continuously. Simulation time is 40 mins
Steady state after 15 minsWLAN scenario in OPNET, 4 APs and 20 Users
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Simulation Results (OPNET)
Overall load on the networkwas reduced by loadbalancing Reduced overallcongestion
After applying load balancing,client 9 associated with BSS2,
and improved its throughput.
Overall load at the network
Throughput of FTP client 9
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2nd Problem
Channel Assignment
Careful consideration must be given to
assigning channels to APs. Otherwise thefollowings may result:
High interference between APs overlapping zones.
Users in the overlapping region of two or more
interfering APs will suffer: Delay
Low data ratesThis is due to the huge increased requests by the userin retransmitting damaged/unsuccessful packets.
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Proposed Solution
Two folds:
Assign channels at the design stage (no
users) with the objective to minimize the totalsumof interference between neighboring APs.
Re-Assign channels when users exist on thenetwork.
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11/09/2007 *Formulation not yet published 25
Problem Formulation (initial stage)
Objective
Subject to
1
,
1
max for eachmin { }MAX iji
M M
SUM ij
i j
jW I iW I j
( )
ij jij
ij
w PI
PL d
1 |Ch Ch | 0.2, for 0where =
0 otherwise
i j ij
ij
ww
i =1, , Mj =1,, Mi j
, {1,.., }
{1,..,11}
j kCh Ch K
K
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Problem Formulation (with users)
Objective
Subject to
1 1
( )N M
ij
i j
Max SIR k
1
( ),M
ij ij jk
j
I P w j k
1 |Ch Ch | 0.2, for 0where =
0 otherwise
i j ij
ij
ww
( ) ,ikijij
PSIR k i jI
, {1, .., }j k M
{1,.. }i N
, {1,.., }
{1,..,11}
j kCh Ch K
K
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Heuristic Algorithm
Apply initial channel assignment
Users enter the networkApply load balancing algorithm based on
power management.
Save final transmitted powers at APs.
Re-compute received signal at users.
Compute SIR.Apply Channel Assignment algorithm based on
SIR.
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Numerical Analysis and Results Initial Approach (based
on min AP interference)
AP Number FCA: Equal PowerFCA: Power
Management
AP1 1 7
AP2 8 1
AP3 3 11
AP4 11 3
Interference (dB) -21.17 -22.02
Scenario 1: 4 APs (12, 18, 20, 17 (dBm))
AP Number FCA: Equal PowerFCA: Power
Management
AP1 11 11
AP2 1 1
AP3 8 7
AP4 4 5
AP5 11 2
AP6 1 10
Interference (dB) -19.15 -25.49
Scenario2: 6 APS (16, 16, 11, 6, 6, 1 (dBm))
AP3AP2
AP1 AP4
4 APs
AP1 AP4 AP5
AP2 AP3 AP6
6 APs
4%
33%
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11/09/2007
* Published at IEEE PIMRC conference
Jun'07 29
Numerical Analysis and Results
Initial Approach (Contd)
Scenario 3: 9 APs (4, 12, 20, 16, 20, 16, 17, 8, 19 (dBm))
AP Number FCA: Equal PowerFCA: Power
Management
AP1 11 11
AP2 4 1
AP3 8 6
AP4 1 1
AP5 11 10
AP6 4 1
AP7 11 11
AP8 1 1
AP9 11 11
Interference (dB) -17 -19.86
9 APs
AP7 AP8 AP9
AP6AP3AP2
AP1 AP4 AP5
* Published at IEEE ICSPC conference Nov07
17%
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Numerical Analysis and Results
Second Approach (based on max SIRat users)
Two special cases:
Many users in the overlapping zone
Users are not in the overlapping zone
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*Submitted to IEEE WCNC conference
Apr'08 31
Numerical Analysis and Results
AP NumberFCA: No Users
(minimize interference
between APs)
FCA: With Users(Maximize SIR at the
Users)
AP1 1 6
AP2 8 11
AP3 3 2
AP4 11 1
Avg. SIR (dB) 6.51 7.66
AP Number FCA: No users FCA: with users
AP1 11 2
AP2 1 11
AP3 8 6
AP4 4 6
AP5 11 8
AP6 1 1
Avg. SIR (dB) 4.22 4.47AP Number FCA: No users FCA: With Users
AP1 11 6
AP2 4 1
AP3 8 11
AP4 1 8
AP5 11 11
AP6 4 4
AP7 11 6
AP8 1 8
AP9 11 11
Avg. SIR (dB) 0.44 2.86
Scenario 1: 4 APs (12, 18, 20, 17 (dBm))
Scenario2: 6 APS (16, 16, 11, 6, 6, 1 (dBm))
Scenario 3: 9 APs (4, 12, 20, 16, 20, 16, 17, 8, 19 (dBm))
17%
6%
540%
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Simulation Scenarios (OPNET)
4-AP WLAN
4-AP WLAN
Scenario1 Scenario2 Scenario3 Scenario4
AP1 1 1 1 6
AP2 2 6 8 11
AP3 3 1 3 2
AP4 4 11 11 1
Summary of the 4 Scenarios
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11/09/2007 *Results not yet published 33
Simulation Results (OPNET)
Same assumptions from the load balancing scenariosapply EXCEPT for the channel assignment.
Zoomed in ViewOverall Upload Response TimeOverall Throughput
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Dynamic Model
Background
No such application of a dynamic user behavior modelon a full scale dynamic network.
Published work related to user behavior reported theuser behavior through monitoring network traffic andbehavior for long periods of time (10 months ormore).
Such a model is significant for future researchers inthe WLAN field or industry where load distributionand channel assignment algorithms can beimplemented and tested on a dynamic scale .
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Dynamic Scenario 1
Scenario 1: Varying data rate with time
4 APs and 20 users.
Data rate of users vary with time according to a normal
distribution (= 4 Mbps, = 2 Mbps). Data rate is captured every 5 minutes.
All users are continuously active.
All APs and users are stationary.
Default AP transmitted power is 20 dBm.
Receivers threshold is -90 dBm.
Simulation period is 2 hours.
Numerical Analysis and Results
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Numerical Analysis and Results
Initial user-AP association
Initial CF Final CF
Final
transmitted
power
(dBm)
Final FCA
AP1 0.2563 0.2464 16 1
AP2 0.0669 0.2721 20 6
AP3 0.3752 0.2502 12 11
AP4 0.3445 0.2741 11 11
82.49% 99.77%
Iteration 1
Initial CF Final CF
Final
transmitted
power
(dBm)
Final FCA
AP1 0.2454 0.3023 20 1
AP2 0.1275 0.3979 16 6
AP3 0.7240 0.3968 5 6
AP4 0.3703 0.3703 18 11
72.94% 98.89%
Last iteration
Final user-AP association
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Dynamic Scenario 2
Scenario 2: Dynamic User Behavior Same assumptions as before apply EXCEPT that the
data rate now is fixed over simulation time.
Users arrive to the WLAN according to a Poissondistribution with an arrival rate of.
varies with time. However, in this scenario has aconstant value over the simulation period (2 hours).
Pr( )!
nen
n
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Dynamic Scenario 2 (Contd)
Session lengths of each user ischaracterized by a Bi-Pareto distribution.
When a users session is over, the user isassumed as either no longer active or leftthe network.
i.e. the user no longer has a data rate it does not
constitute any load at its AP.( 1) 1
( ) (1 ) ( ) ( ),P x k c x x kc x kc x k
Numerical Analysis and Results
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Numerical Analysis and Results
= 4User Number Arrival times(hrs)
Departure
Time(hrs)
21 0.10
22 0.15
23 0.70
24 0.76
25 1.13
26 1.42
10 1.62
27 1.66
3 1.93
28 1.87
29 2.00
Arrival and Departure time Table
4 APs, 20 Users
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Numerical Analysis and Results(Contd)
FCA:
Arrive 4
FCA:
Arrive 5
FCA:
Arrive 6
FCA:
Leave 1
Final Tx
Power
(dBm):
Arrive 4
Final Tx
Power
(dBm:
Arrive 5
Final Tx
Power
(dBm):
Arrive 6
Final Tx
Power
(dBm):
Leave 1
AP1 1 1 1 1 17 20 15 18
AP2 6 6 6 6 11 14 19 18
AP3 11 11 11 11 12 5 19 12
AP4 6 6 1 1 15 13 13 13
98.75% 99.14% 96.89% 99.62%Avg. SIR
(dB)6.46 6.27 6.08 6.05
FCA and Load Balancing results
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Numerical Analysis and Results(Contd)
FCA and Load Balancing results
FCA:
Arrive 7
FCA:
Leave 2
FCA:
Arrive 8
FCA:
Arrive 9
Final Tx
Power
(dBm):
Arrive 7
Final Tx
Power
(dBm):
Leave 2
Final Tx
Power
(dBm):
Arrive 8
Final Tx
Power
(dBm):
Arrive 9
AP1 1 1 1 1 20 19 20 18
AP2 6 6 6 6 18 17 15 18
AP3 11 11 11 11 8 15 16 15
AP4 6 6 1 1 10 18 11 9
99.01% 97.69% 98.92% 99.42%
Total SIR
(dB)5.92 5.94 5.77 5.71
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Numerical Analysis and Results(Contd)
FCA and Load Balancing results
-- Added users
--Removed users
-- Existing users
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Conclusion
A new load balancing algorithm based on powermanagement was developed.
A new channel assignment algorithm based on
maximizing SIR was developed. Results were validated using OPNET simulation
to show the effectiveness of the developedalgorithms.
Dynamic data rate and user behavior wereintroduced to verify the ability of the developedmodels to adapt to these dynamic behaviors.
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Future Work
Extension of the dynamic model tocombine both variable data rate and users
behavior.Application of this work to WiMAX (IEEE
802.16).
Integration of smart antenna technologyat the AP.
Expand developed work to larger WLANs.
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Special Thanks Ph.D. Advising Committee:
Dr.. Hussain Al-Rizzo(Advisor)
Dr. Robert Akl
Dr. Yupo Chan
Dr. Hassan El-Salloukh
Dr. Seshadri Mohan
Dr. Haydar Alshukri
Ph.D. Candidates
Rami Adada Rabindra Ghimire
Graduate Student TJ Calvin
Network Administrator Greg Browning
OPNET Technical Support
LINGO Technical Support