Load Distribution and Channel Assignment in IEEE 802 Load Distribution and Channel Assignment in IEEE 802.11 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

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

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

Introduction Wireless Local Area Networks (WLANs) Airports Hotels Campuses WLANs are divided into 3 categories: 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 11/09/2007 Ph.D. Defense

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 802.11b) Channel Assignment Minimize interference To improve QoS (less delay and higher throughput) 3 non-overlapping channels in IEEE 802.11b/g (1, 6, and 11) Frequency Spectrum for IEEE 802.11b/g 11/09/2007 Ph.D. Defense

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

(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

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.

(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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Future Work Extension of the dynamic model to combine both variable data rate and users’ behavior. Application of this work to WiMAX (IEEE 802.16). Integration of smart antenna technology at the AP. Expand developed work to larger WLANs. 11/09/2007 Ph.D. Defense

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