Using Multiple Channels and Spatial Backoff to Improve Wireless Network Performance

1. Using Multiple Channels and Spatial Backoff to Improve Wireless Network Performance Nitin Vaidya University of Illinois at Urbana-Champaign www.crhc.uiuc.edu/wireless MURI Review Meeting, September 12, 2006

2. Sharing the Spectrum Classification of approaches • Temporal : Traditional contention resolution • Spatial : Spatial backoff • Spectral : Multi-channel systems

3. Multi-Channel Wireless Networks:Capacity withConstrained Channel Assignment Joint work with Vartika Bhandari

4. Channel 1 Channel 2 Channel c Channel Model • c channels available • Bandwidth per channel W

5. 1 1 1 1 c c Channel-Interface Scenarios • Common scenarios today Single interface Multiple interfaces

6. Fewer Interfaces than Channels • An interface can only use one channel at a time Channel 1 Channel c Single interface, multiple channels

7. Interface Constraint • Throughput is limited by total number of interfaces in a neighborhood • Interfaces, a limited resource k nodes in the “neighborhood”  throughput ≤k *W • (for single interface per node)

8. Capacity with Multiple Channels • How does capacity scale when number of channels c is increased? • Depends on constraints on channel assignmentto the interfaces Capacity as defined by [Gupta & Kumar]

9. Unconstrained Channel AssignmentPre-MURI work [Kyasanur05MobiCom] Network Capacity Single interface nodes can utilize multiple channels effectively Channels

10. Constrained Channel Assignment • Hardware limitations • Low cost, low power transceivers • Limited tunability of oscillator • Policy issues • Dynamic spectrum access via cognitive radio:secondary users in a band only when primary inactive

11. Network Model c channels W bandwith per channel s(1) s(2) … … s(f) Each node has one interface n nodes randomly deployed over a unit area torus Interface can switch between f channels: 2 ≤ f ≤ cc = O(log n)

12. Network Model • Motivated by [Gupta & Kumar] • Each node is source of exactly one flow • Chooses its destination as node nearest to a randomly chosen point

13. Impact of Switching Constraints Connectivity: A device can communicate directly with only a subset of the nodes within range (4, 5) (2, 3) (5, 6) (1, 2) (1, 3) (6, 7) (3, 6) Bottleneck formation: Some channels may be scarce in certain regions, causing overload on some channels/nodes (7, 8) (2, 5)

14. Proposed Models • Adjacent (c, f) assignment • A node can use adjacent f channels • Model encompasses untuned radio model • Random (c, f) assignment • A node can use randomly chosen f channels • Spatially correlated assignment

15. Adjacent (c, f) Assignment • Each node assigned a block of adjacent f channels • c – f + 1 possibilities • A node can use channel i with probability = minimum {i, c-i+1, f, c-f+1} /c f=2 c=8

16. Random (c, f) Assignment • Each node uses a random f-subset of channels • A node can use channel i with probability f/c f=2 c=8

17. N randomly located pseudo-nodes, each assigned a channel Nodes close to a pseudo-node blocked from using thepseudo-node’s channel Captures primary-secondary users Spatially Correlated Assignment R 1 R 2

18. Results at a Glance f

19. c Adjacent (c, f) Assignment Necessary condition on range r(n) Capacity upper bound =

20. Lower Bound Construction Divide torus into square cells of area a(n) r(n) Transmission range r(n) Cell structure based on [El Gamal]

21. Lower Bound Construction • Notion of preferred channels: • Probability that a node has that channel is at least f/2c • Includes most channels (except the fringe) • Each node has at least f/2 preferred channels • By choice of a(n): Every cell has Θ(log(n)) nodes capable of switching on each preferred channel w.h.p.

22. Routing of Flows Straight-line routes forlong flows. Detour routing for short FlowsEnsure W(c/f) hops D P S

23. Channel Transition Strategy Start transitions to get to a preferred channel at destination (channel 5) Use randomlychosen preferred channel available at source (channel 2) ( 3, 4, 5) 5 (4, 5, 6) (4, 5, 6) 5 (2, 3, 4) (3, 4, 5) 2 (1, 2, 3) 4 (4, 5, 6) 2 (2, 3, 4) 3 (2, 3, 4) (1, 2, 3) Adjacent (6, 3) assignment Preferred channels : 2,3,4,5

24. Random (c,f) Channel Assignment • Required range for connectivity smaller than adjacent (c,f) • However, at minimum range, all channels not sufficiently represented in each cell • Our lower bound construction is not tight:Uses larger range than minimum for connectivity

25. Conclusion: Multi-Channel Networks f Even when f=2, get capacity benefit of √c

26. Conclusion: Multi-Channel Networks cf cf f Even when f=2, get capacity benefit of √c

27. Conclusion: Multi-Channel Networks • Constrained channel assignment may be mandated by cost/complexity/policy constraints • Possible to get significant benefits with little flexibility in channel switching • Open issues • Closing the gap for random assignment • Spatially correlated assignment • Protocol design

28. Sharing the Spectrum Classification of approaches • Temporal : Traditional contention resolution • Spatial : Spatial backoff • Spectral : Multi-channel systems

29. Spatial Contention ResolutionwithCarrier Sense Protocols Joint work with Xue Yang

30. Contention Resolution • Temporal Approach:Adapt channel access probability number of transmissions in a contention region = 1 • Spatial Approach: Adapt contention region  number of transmissions in a contention region = 1

31. Spatial Approach: Adapt contention region Contention Resolution Temporal Approach: Adapt access probability Number of transmissions in a contention region = 1

32. Larger Occupied Space • Fewer concurrent transmissions at higher rate

33. Smaller Occupied Space • More concurrent transmissions at lower rate

34. Signal Strength distance Carrier Sense Multiple Access (CSMA) • D perceives idle channel although A is transmitting D B C A CS Threshold

35. How Carrier-Sensing Controls Occupied Space B D C A F E Signal Strength CS Threshold distance

36. How Carrier-Sensing Controls Occupied Space • Larger CS threshold by other stations leads to smaller occupied space by station A’s transmissions B D C A F E CS Threshold Signal Strength distance

37. Transmission Rate Needs to Be Adjusted Suitably • Larger CS threshold leads to higher interference • Transmission rate depends on Signal-to-Interference-Noise Ratio Lower rate B D C A F E CS Threshold Signal Strength distance

38. Adaptation of Occupied Space • Occupied Space == Contention Region • Occupied space can be adapted by joint adaptations: Rate-CS threshold Power-CS threshold Power-rate Power-rate-CS threshold

39. Analytical Motivation for ProtocolsPre-MURI work [Yang05Infocom] Cellular Model + Carrier Sense SINR

40. β = CSth / Rx th (dB) Network Aggregate Throughput(curves for different network parameters) For fixed power, optimal needs joint rate and CS threshold adaptation Normalized Aggregate Throughput

41. Dynamic Spatial Backoff For fixed power, optimal needs joint rate and CS threshold adaptation Joint adaptation of other parameters can be justified similarly

42. Dynamic Spatial BackoffJoint Rate and CS Threshold Adaptation • Adaptation as search Rate[k] Rate[k-1] 2 dimensional space Rate Rate[2] Rate[1] CS[1] CS[2] CS[k-1] CS[k] CS Threshold

43. Towards a Protocol:Reduce Search Space • Reduce search space using a lower bound on suitable CS threshold for a given rate Rate[k] Rate Rate[1] CS[1] CS[2] CS[k-1] CS[k] CS Threshold

44. Rate Rate rate[4] rate[4] V V rate[3] rate[3] V V rate[2] rate[2] V V rate[1] rate[1] CS[1] CS[1] CS[2] CS[2] CS[3] CS[3] CS[4] CS[4] CS Threshold CS Threshold > > > > > > Towards a Protocol: Dynamic SearchUsing Transmission Success/Failure History Failure Success

45. Towards a Protocol: Other Components • Determining success or failure using current parameters • Using history to guide search Successful combination of parameters cached for future use

46. Towards a Protocol • We have proposed a dynamic spatial backoff protocol that adapts rate and CS threshold • Similar mechanisms can be used for other joint adaptations

47. β = CSth / Rx th (dB) Performance of Dynamic Spatial Backoff(Random Topology: 40 nodes) 101% of static optimal Aggregate Throughput (Mbps)

48. β = CSth / Rx th (dB) Performance of Dynamic Spatial Backoff(Random Topology: 16 nodes) 92% of static optimal Aggregate Throughput (Mbps)

49. Conclusion: Dynamic Spatial Backoff • Significant potential to optimize performanceusing distributed mechanisms • Challenges remain:Accurately determining success versus failure Fully distributed mechanisms can be sub-optimal Interactions with higher layers Integration with temporal contention resolution

50. Thanks! www.crhc.uiuc.edu/wireless