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Load Distribution and Channel Assignment in IEEE 802.11 Wireless Local Area Networks

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

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Load Distribution and Channel Assignment in IEEE 802.11 Wireless Local Area Networks

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  1. 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

  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) 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 Ph.D. Defense

  4. 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 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 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 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. 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. 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 Ph.D. defense

  10. 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 End of list Results Add user to list Remove user from list A New Heuristic Algorithm 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. 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. Ph.D. Defense

  13. Problem Formulation • MCAP NLIP formulation for j= 1,…, M for i= 1,…,N 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 . Ph.D. Defense

  15. 4 1 2 Numerical Analysis and Results User-AP candidate association • Receiver Sensitivity at the user is -90 dBm • Transmitted Power at each AP is 20 dbm Ph.D. Defense

  16. Service Area Map Numerical Analysis and Results (Cont’d) Data rate of users • Traffic is randomly generated between 1 Mbps and 6 Mbps for each user Ph.D. Defense

  17. 1 1 1 Numerical Analysis and Results Optimal user-AP association • Each user is associated to one and ONLY one AP. Ph.D. Defense

  18. Numerical Analysis and Results (Cont’d) 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. Ph.D. Defense

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

  20. 9 APs 4 APs 16 APs Numerical Analysis and Results (Cont’d) *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 *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 Ph.D. Defense

  23. This is due to the huge increased requests by the user in retransmitting damaged/unsuccessful packets. 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 Ph.D. Defense

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

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

  26. Problem Formulation (with users) • Objective • Subject to 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. Ph.D. Defense

  28. 4% 4 APs AP2 AP3 AP6 6 APs AP1 AP4 AP5 33% AP2 AP3 AP1 AP4 Numerical Analysis and Results Scenario 1: 4 APs (12, 18, 20, 17 (dBm)) • Initial Approach (based on min AP interference) Scenario2: 6 APS (16, 16, 11, 6, 6, 1 (dBm)) Ph.D. Defense

  29. 9 APs 17% AP7 AP8 AP9 AP2 AP3 AP6 AP1 AP4 AP5 Numerical Analysis and Results Scenario 3: 9 APs (4, 12, 20, 16, 20, 16, 17, 8, 19 (dBm)) • Initial Approach (Cont’d) * Published at IEEE ICSPC conference Nov’07 * 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 Ph.D. Defense

  31. 17% 6% 540% Numerical Analysis and Results 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)) *Submitted to IEEE WCNC conference Apr'08

  32. Simulation Scenarios (OPNET) • 4-AP WLAN Summary of the 4 Scenarios 4-AP WLAN 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 *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 . 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. Ph.D. Defense

  36. Numerical Analysis and Results Iteration 1 Initial user-AP association Last iteration Final user-AP association *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). 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. Arrival and Departure time Table Numerical Analysis and Results • = 4 4 APs, 20 Users Ph.D. Defense

  40. Numerical Analysis and Results (Cont’d) FCA and Load Balancing results Ph.D. Defense

  41. Numerical Analysis and Results (Cont’d) FCA and Load Balancing results *Results not yet published

  42. Numerical Analysis and Results (Cont’d) FCA and Load Balancing results -- Added users --Removed users -- Existing users 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. 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 802.16). • Integration of smart antenna technology at the AP. • Expand developed work to larger WLANs. Ph.D. Defense

  45. Ph.D. Advising Committee: Dr.. Hussain Al-Rizzo (Advisor) Dr. Robert Akl Dr. Yupo Chan Dr. Hassan El-Salloukh Dr. Seshadri Mohan Dr. HaydarAlshukri Ph.D. Candidates RamiAdada RabindraGhimire Graduate Student TJ Calvin Network Administrator Greg Browning OPNET Technical Support LINGO Technical Support Special Thanks Ph.D. Defense

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