1. Peter A. Steenkiste & Dina Papagiannaki 1 18-759: Wireless Networks L ecture 15: WiFi Self-Organization Dina Papagiannaki & Peter Steenkiste Departments of Computer Science and Electrical and Computer Engineering Spring Semester 2009 http://www.cs.cmu.edu/~prs/wireless09/

2. Peter A. Steenkiste & Dina Papagiannaki 2 Overview l Self-organization/Management of WiFi networks l Urban, cooperative environments (unmanaged) »Frequency selection »User association »Power control l Enteprise WLANs (managed) l New application domains (unmanaged+managed)

3. Peter A. Steenkiste & Dina Papagiannaki 3 Design of the Wired Internet Overprovisioning the solution of choice!

4. Peter A. Steenkiste & Dina Papagiannaki 4 From a managed core to an unmanaged edge A large fraction of the Internet’s clients are going wireless (WiFi, 3G, WiMax) Client performance primarily determined by the edge network 802.11 networks challenge our traditional thinking in network design and management The need for measurement paramount due to the unreliability and dynamics of the medium, the notion of contention domains and mobility

5. Peter A. Steenkiste & Dina Papagiannaki 5 What determines client performance? l Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) Transmitter senses the medium, uses Clear Channel Assessment (CCA) threshold to determine state If medium idle, randomize access When backoff counter=0, transmit Upon ACK, reset backoff counter If no ACK, double contention window, adjust transmission rate and try again Performance = f(access probability, retransmissions, link quality, hidden terminals)

6. Peter A. Steenkiste & Dina Papagiannaki 6 Client throughput 54 Mbps Effective throughput~ 30 Mbps 11 Mbps Effective throughput< 10 Mbps 1 Mbps Effective throughput ~Kbps

7. Peter A. Steenkiste & Dina Papagiannaki 7 Solutions and operations Minimize the number of transmitters in the same contention domain Ensure high quality links between clients and APs Minimize the effect of hidden terminals Frequency selection Power Control User Association RTS/CTS Scheduling

8. Peter A. Steenkiste & Dina Papagiannaki 8 Minimize the number of transmitters in the same contention domain Frequency selection Power Control

9. Peter A. Steenkiste & Dina Papagiannaki 9 WiFi frequency selection l 2.4 GHz band (802.11b/g) – 11 channels, 3 orthogonal l 5 GHz band (802.11a) – 11/12 channels depending on continent l Question: Which frequency should each AP operate on for optimal performance?

10. Peter A. Steenkiste & Dina Papagiannaki 10 Cellular analogue

11. Peter A. Steenkiste & Dina Papagiannaki 11 3 802.11b/g frequencies not enough

12. Peter A. Steenkiste & Dina Papagiannaki 12 WiFi scanning tools: NetStumbler

13. Peter A. Steenkiste & Dina Papagiannaki 13 Frequency Selection (1, 6, 11)

14. Peter A. Steenkiste & Dina Papagiannaki 14 client AP Mbps/11gClient1Client2Client3 Before11.816.8614.37 After30.51 (*3)30 (*5)29.45 (*2) Effect of Frequency Selection

15. Peter A. Steenkiste & Dina Papagiannaki 15 Alternatives l Interference is the outcome of transmissions! l Frequency selection could take workload into account »Requires additional measurements »Could lead to instability if based on variable measurements

16. Peter A. Steenkiste & Dina Papagiannaki 16 Partially overlapping channels

17. Peter A. Steenkiste & Dina Papagiannaki 17 Using partially overlapping channels Partially overlapped channels not considered harmful, In ACM Sigmetrics 2006

18. Peter A. Steenkiste & Dina Papagiannaki 18 Minimize the number of transmitters in the same contention domain Frequency selection Power Control

19. Peter A. Steenkiste & Dina Papagiannaki 19 Power Control in 802.11 l Heterogeneous transmit powers across nodes can lead to node starvation! 1 st order starvation We need to ensure that there is symmetry in the nodes’ contention domains.

20. Peter A. Steenkiste & Dina Papagiannaki 20 What is the benefit of power control? l Reducing transmission power can reduce interference in the network l Increasing transmission power can improve client SINR thus allowing for higher transmission rates l There is a tradeoff between the amount of interference we introduce in the network and the additional throughput benefit at the client

21. Peter A. Steenkiste & Dina Papagiannaki 21 Condition for starvation free power control l We need to ensure network symmetry l We have proven that for starvation-free power control we need to keep the product of CCA threshold and transmission power constant l CCA * P = C l The louder you are going to shout the more carefully you should listen for the nodes that whisper Interference Mitigation through Power Control in High Density 802.11 WLANs, IEEE Infocom 2007

22. Peter A. Steenkiste & Dina Papagiannaki 22 client AP Mbps/11gClient1Client2Client3 Before 11.816.8614.37 After 29.45 (*3)22.59 (*4)30.51 (*2) Effect of Power Control

23. Peter A. Steenkiste & Dina Papagiannaki 23 Ensure high quality links between clients and APs User Association

24. Peter A. Steenkiste & Dina Papagiannaki 24 User throughput Internet 1.Channel access time 2.Aggregated transmission delay 3.Wireless channel quality State of the art can lead to unnecessarily low throughput!

25. Peter A. Steenkiste & Dina Papagiannaki 25 User association Balance the user associations for minimal potential delay fairness. Users take into account the personal and social cost of different association rules. Mbps/11gClient1 Before~ 5 After~ 8

26. Peter A. Steenkiste & Dina Papagiannaki 26 Overall network fairness improved Mean:1428, variance:4378031 Mean:1559, variance: 627638

27. Peter A. Steenkiste & Dina Papagiannaki 27 Implementation and Experimental set-up l The 3 algorithms are implemented »for both APs and clients »on Intel 2915 prototype driver and firmware l Testbed A : U Cambridge, UK »21 APs, 30 client l Technical Characteristics »Nodes: Soekris net4826, »Wireless cards: Intel 2915 a/b/g –5-dBi omnidirectional antennae

28. Peter A. Steenkiste & Dina Papagiannaki 28 Impact of different algorithms

29. Peter A. Steenkiste & Dina Papagiannaki 29 Minimize the effect of hidden terminals RTS/CTS Scheduling

30. Peter A. Steenkiste & Dina Papagiannaki 30 The transformation of enterprise WLANs Centralization of the control – increased security, opportunity for optimal configuration

31. Peter A. Steenkiste & Dina Papagiannaki 31 Hidden and Exposed Terminals WLANsHP LabsSeoul National University Our Testbed Exposed Terminals 39%9%39% Hidden Terminals 43%70%35% In 30% of the hidden terminals (300 cases) performance degradation was greater than 90%!

32. Peter A. Steenkiste & Dina Papagiannaki 32 Conflict Graph and its measurement Micro-probing can measure the conflict graph of a 20 node network in 20 seconds! No client modifications As accurate as state of the art with ~400 times less overhead Carrier-sense Interference

33. Peter A. Steenkiste & Dina Papagiannaki 33 Centralized Scheduling (Y,C2) (Y,C3) (X,C4) (X,C1) Scheduler 1234 C1 C4 C3 C2 X Y

34. Peter A. Steenkiste & Dina Papagiannaki 34 Implementation Issues Need for a conflict graph Tight synchronization among APs Precise knowledge of when a transmission will be over (not easy due to retransmission and changes in transmission rate) Speculation is required! DCF performs better in the general case!

35. Peter A. Steenkiste & Dina Papagiannaki 35 Hybrid Scheduling Use speculative centralized scheduling only for hidden terminals

36. Peter A. Steenkiste & Dina Papagiannaki 36 The network design space Network density management Frequency selection, power control Centralized control and scheduling More devices on the same frequency overhearing PHY coding conscious choice reachability, minimal handoff time power control, user association No single design will fit-all – understanding constraints and solution space is essential. Applications will be the main drivers

37. Peter A. Steenkiste & Dina Papagiannaki 37

38. Peter A. Steenkiste & Dina Papagiannaki 38 The FON model – large scale WiFi collaboration homeEnterprise Neighborhood Network Management degree

39. Peter A. Steenkiste & Dina Papagiannaki 39 Next-generation community networks Community networks today seen as infrastructure for Internet access … and services on the move [Cabernet, ViFi, Dome] What if such networks formed the new Internet edge with the ability to provide better and new types of services?

40. Peter A. Steenkiste & Dina Papagiannaki 40 Community WiFi

41. Peter A. Steenkiste & Dina Papagiannaki 41 New Types of Services in Neighborhood WiFi Reliability through broadband provider diversity Higher uplink capacity through wireless-assisted broadband link aggregation Services beyond the limitations of one home’s resources Most broadband connections underutilized Wireless speeds far exceed last mile

42. Peter A. Steenkiste & Dina Papagiannaki 42 Today’s home use Internet “broadband”

43. Peter A. Steenkiste & Dina Papagiannaki 43 Large uploads are very slow! Internet !?! zzz..

44. Peter A. Steenkiste & Dina Papagiannaki 44 Burstable broadband service Internet

45. Peter A. Steenkiste & Dina Papagiannaki 45 Burstable broadband service Internet

46. Peter A. Steenkiste & Dina Papagiannaki 46 Burstable broadband service

47. Peter A. Steenkiste & Dina Papagiannaki 47 Aggregation through wireless l What is the best strategy to get efficient aggregation of bandwidth? l Problem: Individual losses can significantly harm performance. Wireless unicast

48. Peter A. Steenkiste & Dina Papagiannaki 48 Opportunistic wireless reception Wireless unicast Opportunistic broadcast ? ? ? Solution needs to minimize redundancy in wired transmissions while making optimal use of the wireless medium

49. Peter A. Steenkiste & Dina Papagiannaki 49 Link-alike offers significant gains through opportunism

50. Peter A. Steenkiste & Dina Papagiannaki 50 Open Questions How could such a mechanism be adjusted for real-time content, such as high resolution video conferencing? Automated frequency selection restricts the number of APs in the same frequency, limiting the potential for overhearing What are the mechanisms needed for optimal performance of neighborhood networks?

51. Peter A. Steenkiste & Dina Papagiannaki 51

52. Peter A. Steenkiste & Dina Papagiannaki 52 US Spectrum Allocation WiFi 700 MHz and white spaces

53. Peter A. Steenkiste & Dina Papagiannaki 53 References l Partially-overlapped Channels not considered harmful, Arunesh Mishra, Vivek Shrivastava, Suman Banerjee, William Arbaugh. In ACM Sigmetrics, St. Malo, France, June 2006. l MDG: Measurement-Driven Guidelines for 802.11 WLAN Design, I. Broustis, K. Papagiannaki, S. Krishnamurthy, M. Faloutsos, and V. Mhatre. In ACM Mobicom, Montreal, Canada, September 2007. l Interference Mitigation through Power Control in High Density 802.11 WLANs, V. Mhatre, K. Papagiannaki and F. Baccelli. In IEEE Infocom, Anchorage, Alaska, May, 2007. l Measurement-Based Self Organization of Interfering 802.11 Wireless Access Networks, B. Kaufmann, F. Baccelli, A. Chaintreau, V. Mhatre, K. Papagiannaki and C. Diot. In IEEE Infocom, Anchorage, Alaska, May, 2007. l Link-alike: Using Wireless to Share Network Resources in a Neighborhood, S. Jakubczak, D. Andersen, M. Kaminsky, K. Papagiannaki, and S. Seshan. To appear in ACM Sigmobile Mobile Computing and Communications Review (MC2R)