1. Load Distribution and Channel Assignment in IEEE 802
Load Distribution and Channel Assignment in IEEE Wireless Local Area Networks
Ph.D. Dissertation Defense
Presented 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

2. Presentation Outline Introduction Wireless Local Area Networks (WLANs)
Access Points (APs) Congestion
Channel Assignment
Related Work
Contributions
Problems Statements
1. Congestion Problem
Proposed Solution
Problem Formulation
Algorithm
Numerical Analysis and Results
Simulations (OPNET)
11/09/2007
Ph.D. Defense

3. Presentation Outline (Cont’d)
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
11/09/2007
Ph.D. Defense

4. Introduction Wireless Local Area Networks (WLANs)
Airports
Hotels
Campuses
WLANs are divided into 3 categories:
IEEE a in the 5 GHz band (54 Mbps)
IEEE b in the 2 GHz band (11 Mbps)
IEEE g in the 2 GHz band (54 Mbps)
Example of WLAN
11/09/2007
Ph.D. Defense

5. Introduction (Cont’d)
What is Access Point (AP) congestion?
Some times referred to as “Hot Spot”
CAP= (R1+ R2+..+ RN)/BW
CAP: Congestion at AP
R : Data rate of a user connected to the AP
BW: Bandwidth (11 Mbps for IEEE b)
Channel Assignment
Minimize interference
To improve QoS (less delay and higher throughput)
3 non-overlapping channels in IEEE b/g (1, 6, and 11)
Frequency Spectrum for IEEE b/g
11/09/2007
Ph.D. Defense

6. Limitation of Previous Research
AP Placement
The main objective was to use a minimum number of APs for adequate 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 or maximizing 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.
None of the work I am aware of dealt with Min the Sum and Min the Max at the same time.
**ONLY one work I am aware of that integrated both.
11/09/2007
Ph.D. Defense

7. (Cont’d) 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 by redistributing users.
Improves the load ONLY at the MCAP.
Load balanced agents installed at the APs that broadcast periodically 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.
* I am only aware of one paper that did that
11/09/2007
Ph.D. Defense

8. 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 the SIR at the users.
Users involved in the assignment of channels.
Different user distributions will lead to different channel assignment.

9. (Cont’d)
Combining both load balancing based on power management and the channel assignment based on SIR: A Novel Scheme.
Verified the performance predicted from optimization versus realistic OPNET-based network simulations: New contribution
Developed a realistic dynamic model approach that accounts for variable users’ data rates and users’ behavior: New contribution
11/09/2007
Ph.D. defense

10. A New Heuristic Algorithm
Initial Channel Assignment
Users enter to network
Load Balancing based on PM
Re-Assign channels based on SIR
Sort arriving users and departing users
in ascending order in a list
Check list
Arrive
Depart
Add user to list
Remove user from list
Results
End of list
11/09/2007
Ph.D. Defense

11. 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.
11/09/2007
Ph.D. Defense

12. Proposed Solution
Reduce congestion at the hot spots by decrementing the power transmitted by the Most Congested AP (MCAP) in discrete steps until one or more users can no longer associate with any AP or their data rate can no longer be accommodated.
The final transmitted power of each AP is set to the best balance index, , achieved.
Advantages:
Load is fairly distributed.
Increase in data rate throughput per user.
Less adjacent and co-channel interference.
11/09/2007
Ph.D. Defense

13. Problem Formulation MCAP NLIP formulation for j= 1,…, M for i= 1,…,N
11/09/2007
Ph.D. Defense

14. Algorithm
Compute Received Signal Strength Indicator (RSSI) at each user.
Generate a binary matrix that assigns “1” if a user’s 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 no longer be accommodated.
Observe the power levels at each AP and the best user’s association at the best .
11/09/2007
Ph.D. Defense

15. Numerical Analysis and Results
User-AP candidate association
User Number
AP1
AP2
AP3
AP4 1 2 3 4 5 6 7 8 9 10
Receiver Sensitivity at the user is -90 dBm
Transmitted Power at each AP is 20 dbm 4 1 2
11/09/2007
Ph.D. Defense

16. Numerical Analysis and Results (Cont’d)
Data rate of users
Traffic is randomly generated between 1 Mbps and 6 Mbps for each user
User Number
Traffic (Kbps) 1
1752 2
5698 3
4265 4
1994 5
3558 6
3176 7
5319 8
1559 9
2982 10
2263
Service Area Map
11/09/2007
Ph.D. Defense

17. Numerical Analysis and Results
Optimal user-AP association
User Number
AP1
AP2
AP3
AP4 1 2 3 4 5 6 7 8 9 10
Each user is associated to one and ONLY one AP. 1
11/09/2007
Ph.D. Defense

18. Numerical Analysis and Results (Cont’d)
Congestion Factor comparison
Initial Congestion factor:
(No Power Mgmt)
Congestion factor solution according to [2]
Congestion factor with Power Mgmt
AP1
0.6319
0.5234
0.3793
AP2
0.4100
0.3617
AP3
0.2117
0.3167
AP4
0.2026
0.3110
0.3985 
81.15%
90.84%
99.31%
Load is distributed fairly among APs.
Final transmitted power levels at each AP is: 12 dBm, 18 dBm, 20 dBm and 17 dBm, respectively.
11/09/2007
Ph.D. Defense

19. Numerical Analysis and Results (Cont’d)
Service area map after Power Mgmt
Different radii sizes after power adjustment
Users do NOT always associate to the closest AP.
11/09/2007
Ph.D. Defense

20. Numerical Analysis and Results (Cont’d)
4 APs
9 APs
16 APs
11/09/2007
*published at IEEE Sarnoff Conference May'07

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 mins
WLAN scenario in OPNET, 4 APs and 20 Users
11/09/2007
*Not published yet

22. Simulation Results (OPNET)
Overall load on the network was reduced by “load balancing” Reduced overall congestion
After applying load balancing, client 9 associated with BSS2, and improved its throughput.
Overall load at the network
Throughput of FTP client 9
11/09/2007
Ph.D. Defense

23. 2nd Problem Channel Assignment
Careful consideration must be given to assigning channels to APs. Otherwise the followings may result:
High interference between APs’ overlapping zones.
Users in the overlapping region of two or more interfering APs will suffer:
Delay
Low data rates
This is due to the huge increased requests by the user in retransmitting damaged/unsuccessful packets.
11/09/2007
Ph.D. Defense

24. Proposed Solution Two folds:
Assign channels at the design stage (no users) with the objective to minimize the total sum of interference between neighboring APs.
Re-Assign channels when users exist on the network.
11/09/2007
Ph.D. Defense

25. Problem Formulation (initial stage)
Objective
Subject to
i = 1, …, M
j = 1,…, M
i  j
11/09/2007
*Formulation not yet published

26. Problem Formulation (with users)
Objective
Subject to
11/09/2007
Ph.D. Defense

27. Heuristic Algorithm Apply initial channel assignment
Users enter the network
Apply 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.
11/09/2007
Ph.D. Defense

28. Numerical Analysis and Results
Initial Approach (based on min AP interference)
Scenario 1: 4 APs (12, 18, 20, 17 (dBm))
AP3
AP2
AP1
AP4
4 APs
AP Number
FCA: Equal Power
FCA: Power Management
AP1 1 7
AP2 8
AP3 3 11
AP4
Interference (dB)
-21.17
-22.02 4%
Scenario2: 6 APS (16, 16, 11, 6, 6, 1 (dBm))
AP Number
FCA: Equal Power
FCA: Power Management
AP1 11
AP2 1
AP3 8 7
AP4 4 5
AP5 2
AP6 10
Interference (dB)
-19.15
-25.49
AP1
AP4
AP5
AP2
AP3
AP6
6 APs
33%
11/09/2007
Ph.D. Defense

29. Numerical Analysis and Results
Initial Approach (Cont’d)
Scenario 3: 9 APs (4, 12, 20, 16, 20, 16, 17, 8, 19 (dBm))
AP Number
FCA: Equal Power
FCA: Power Management
AP1 11
AP2 4 1
AP3 8 6
AP4
AP5 10
AP6
AP7
AP8
AP9
Interference (dB)
-17
-19.86
9 APs
AP7
AP8
AP9
AP6
AP3
AP2
AP1
AP4
AP5
17%
* Published at IEEE ICSPC conference Nov’07
11/09/2007
* Published at IEEE PIMRC conference Jun'07

30. Numerical Analysis and Results
Second Approach (based on max SIR at users)
Two special cases:
Many users in the overlapping zone
Users are not in the overlapping zone
11/09/2007
Ph.D. Defense

31. Numerical Analysis and Results
Scenario 1: 4 APs (12, 18, 20, 17 (dBm))
Scenario2: 6 APS (16, 16, 11, 6, 6, 1 (dBm))
AP Number
FCA: No Users (minimize interference between APs)
FCA: With Users (Maximize SIR at the Users)
AP1 1 6
AP2 8 11
AP3 3 2
AP4
Avg. SIR (dB)
6.51
7.66
AP Number
FCA: No users
FCA: with users
AP1 11 2
AP2 1
AP3 8 6
AP4 4
AP5
AP6
Avg. SIR (dB)
4.22
4.47
17%
AP Number
FCA: No users
FCA: With Users
AP1 11 6
AP2 4 1
AP3 8
AP4
AP5
AP6
AP7
AP8
AP9
Avg. SIR (dB)
0.44
2.86 6%
Scenario 3: 9 APs (4, 12, 20, 16, 20, 16, 17, 8, 19 (dBm))
540%
11/09/2007
*Submitted to IEEE WCNC conference Apr'08

32. Simulation Scenarios (OPNET)
4-AP WLAN
Summary of the 4 Scenarios
Scenario 1
Scenario 2
Scenario 3
Scenario 4
AP1 1 6
AP2 2 8 11
AP3 3
AP4 4
4-AP WLAN
11/09/2007
Ph.D. Defense

33. Simulation Results (OPNET)
Same assumptions from the load balancing scenarios apply EXCEPT for the channel assignment.
Overall Throughput
Overall Upload Response Time
Zoomed in View
11/09/2007
*Results not yet published

34. Dynamic Model Background
No such application of a dynamic user behavior model on a full scale dynamic network.
Published work related to user behavior reported the user behavior through monitoring network traffic and behavior for long periods of time (10 months or more).
Such a model is significant for future researchers in the WLAN field or industry where load distribution and channel assignment algorithms can be implemented and tested on a dynamic scale .
11/09/2007
Ph.D. Defense

35. 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.
Receiver’s threshold is -90 dBm.
Simulation period is 2 hours.
11/09/2007
Ph.D. Defense

36. Numerical Analysis and Results
Iteration 1
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 
82.49%
99.77%
Initial user-AP association
Last iteration
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
AP4
0.3703 18 11 
72.94%
98.89%
Final user-AP association
11/09/2007
*Results not yet published

37. 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 Poisson distribution with an arrival rate of .
 varies with time. However, in this scenario  has a constant value over the simulation period (2 hours).
11/09/2007
Ph.D. Defense

38. Dynamic Scenario 2 (Cont’d)
Session lengths of each user is characterized by a Bi-Pareto distribution.
When a user’s session is over, the user is assumed as either no longer active or left the network.
i.e. the user no longer has a data rate  it does not constitute any load at its AP.
11/09/2007
Ph.D. Defense 38

39. Numerical Analysis and Results
= 4
Arrival and Departure time Table 26
1.42 10
1.62 27
1.66 3
1.93 28
1.87 29
2.00
User Number
Arrival times(hrs)
Departure Time(hrs) 21
0.10 22
0.15 23
0.70 24
0.76 25
1.13
4 APs, 20 Users
11/09/2007
Ph.D. Defense

40. Numerical Analysis and Results (Cont’d)
FCA and Load Balancing results
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 17 20 15 18
AP2 6 11 14 19
AP3 12 5
AP4 13 
98.75%
99.14%
96.89%
99.62%
Avg. SIR (dB)
6.46
6.27
6.08
6.05
11/09/2007
Ph.D. Defense

41. Numerical Analysis and Results (Cont’d)
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 20 19 18
AP2 6 17 15
AP3 11 8 16
AP4 10 9 
99.01%
97.69%
98.92%
99.42%
Total SIR (dB)
5.92
5.94
5.77
5.71
11/09/2007
*Results not yet published

42. Numerical Analysis and Results (Cont’d)
FCA and Load Balancing results
-- Added users
-- Removed users
-- Existing users
11/09/2007
Ph.D. Defense

43. Conclusion
A new load balancing algorithm based on power management 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 developed algorithms.
Dynamic data rate and user behavior were introduced to verify the ability of the developed models to adapt to these dynamic behaviors.
11/09/2007
Ph.D. Defense

44. Future Work
Extension of the dynamic model to combine both variable data rate and users’ behavior.
Application of this work to WiMAX (IEEE ).
Integration of smart antenna technology at the AP.
Expand developed work to larger WLANs.
11/09/2007
Ph.D. Defense

45. Special Thanks Ph.D. Advising Committee: Network Administrator
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
11/09/2007
Ph.D. Defense