1. 1 Improving Performance of Wireless Networks Nitin Vaidya Joint work with Fan Wu, Tae Hyun Kim, Jian Ni, Vijay Raman, R. Srikant November 4, 2010

2. 2 What Makes Wireless Networks Interesting? Many forms of diversity Time Route Antenna Spatial Channel

3. 3 Multi-Channel Environments Available spectrum 234 … c Spectrum divided into channels 1

4. Multi-Channel Wireless Networks Benefits of channelization  Channel diversity Gain variations Interference mitigation  Channel access efficiency gain 4

5. Recent Contributions on Multi-Channel Networks  Incorporating opportunism in multi-channel networks  Improving channel utilization  Game theoretic approach for channel management 5

6. Opportunistic Routing

7. Opportunism  Traditional routing: S  R  D  But D may sometimes overheard S  R transmission  No need to forward such packets on R  D 7 SRD

8. Opportunism using MORE  Source sends linear combinations of packets in batches  Forwarders keep all heard packets in a buffer  Nodes transmit linear combinations of buffered packets  Destination decodes once it receives enough combinations SRD P1 P2 P3 P1P2P3 =+ b+ c a a,b,c 2,1,3 0,2,1 2,1,3 P1 P2 P3 =+ 1+ 32 2,1,3P1P2P3 =+ 2+ 10 0,2,1P1P2P3 =+ 0+ 23 3,0,23,0,2 3,0,23,0,2 =2 + 1 0,2,17,4,9 2,1,3 + 1 3,0,23,0,2 7,4,97,4,9 =2 + 2 0,2,11,6,61,6,6 2,1,3 - 1 3,0,23,0,2 1,6,61,6,6 P1 P2 P3

9. Opportunism versus Concurrency  For opportunistic scheme to work, nodes must be on the same channel  Reduces concurrency 9 SRD

10. Trade-Off 10 AdvantagesDisadvantages Opportunism Exploits broadcast nature Reduces average # hops Fewer transmissions Higher contention No multiple channel support Multichannel Concurrency Lower contention No opportunistic overhearing Potentially longer routes

11. Example  Traditional Channel Assignment S A D 0.25 0.5 B 0.25 0.75 C1 C2 C3 0.75 C3 0.9 End-to-end throughput = 0.5 Loss probability

12. “Opportunism-Aware” Channel Assignment S A D 0.25 0.5 B 0.25 0.75 C1 C2 0.75 C2 0.9 C1C2 End-to-end throughput = 0.6475

13. Our Contribution  Take into account both opportunistic gains obtained by assigning identical channels to the nodes, as well as concurrency gains by assigning different channels  Extended MORE to a multi-radio multi-channel (MRMC) environment 13

14. Summary  Opportunistic schemes can benefit in multi-channel environments  Channel assignment needs to be opportunism-aware  Proposed such an assignment scheme 14

15. 15 Packet Size-Dependent Channel Selection

16. Channel Width  Typically, channels are assumed identical width  May benefit by varying channel widths 16 234 … c 1

17. Motivation 17 Rate-independent MAC overhead L 1 bits DIFSHeader L 1 /R L 2 bitsDIFSHeader T L 2 /R

18. MAC Overhead vs Packet Size 18 Packet size L i T = 50μs; R = 54 Mbps

19. Current Approach  Frame Aggregation (used in IEEE 802.11n)  Aggregate and send multiple packets in a single transmission opportunity 19 L 1 bits DIFSHeader L 2 bitsL 3 bits overhead Multiple packets to amortize overhead

20. Packet Size-Dependent Channel Widths 20  Partition a channel into narrow and wide sub-channels  Use narrow sub-channel for short packets  Use wide sub-channel for long packets

21. Proof-of-Concept  Consider a node (A) communicating with multiple other nodes 21 A

22. Proposed Approach 22 1 Clients estimate own short packet load, and inform node A Node A estimates aggregate short packet load 2 Node A determines partition {BW S, BW L } 3 Clients use BW S for short & BW L for long packets 4

23. Summary  Channel width selection based on packet size distribution  Can perform better than frame aggregation  Ideas can be extended to arbitrary networks 23

24. CSMA with Imperfect Carrier Sensing

25. Carrier Sensing (CS)  Not perfect  With narrower channels and mobility, fading can be an issue  What happens to network performance when CS is imperfect ? 25

26. Throughput-Optimal Schedulers  A scheduler is throughput-optimal if it can serve all schedulable traffic  Throughput-optimal scheduler by Tassiulas-Ephremides’92 Schedule = Computationally complex and centralized solution

27. Related Work  Continuous-time CSMA-like algorithm by Jiang-Walrand’08  Discrete-time CSMA by Ni-Srikant’09

28. Our Contribution: Preemptive CSMA  Discrete-time medium access  Per-packet scheduling decision  Data packet collisions modeled  Non-zero carrier sense time Analysis for  Perfect carrier sensing  Imperfect carrier sensing

29. Model  Link-centric model  Transmission rate is normalized to 1  One-hop traffic  Conflict graph to model interference

30. Medium Access Model Last α-duration of each time slot for carrier sense

31. Preemptive CSMA  Two access probabilities: a i and p i Carrier sense u(t): preemption x(t): transmission schedule C i : set of conflict links of i ACK reception

32. Performance Analysis  Schedule evolution: discrete-time reversible Markov chain Stationary distribution  C u : set of conflicting links of links in u  When p i = 1 - = exp{w i (q i )} -1 exp{w i (q i )} 1 exp{w i (q i )}

33. Throughput-Optimality  Preemptive CSMA is throughput-optimal  When access probabilities are 0 < a LB ≤ a i ≤ a UB < 1 p i = 1 - 1/exp{w i (q i )} where w i is a strict concave function with w i (0) = 0  Proof relies on time-scale separation At each time slot, the Markov chain in the steady state

34. Carrier Sense Failure  i.i.d. failure events over time slots and links  Two types of carrier sense failures False positive –No activity, but busy state sensed –False positive with probability η False negative: –Activity, but idle state sensed –False negative with probability γ

35. Carrier Sense Failure: Main Result  By choosing small enough access probability, possible to stabilize arbitrarily large fraction of capacity region Proof complexity: Markov chain is no longer reversible Use perturbation theory for Markov chains

36. Summary Preemptive CSMA  Good performance achievable despite imperfect carrier sensing  Small access probability overcomes the effect of carrier sensing failures

37. Where are we now ? 37

38. 38 What Makes Wireless Networks Interesting? Many forms of diversity Time Route Antenna Spatial Channel

39. Wireless Diversity  This project has furthered our understanding of approaches to wireless diversity using suitable protocols  We now have a better understanding of cross-layer protocol design 39

40. What Remains?  Physical layer community has also been making significant progress –Interference alignment –Cooperation –Security  Need to incorporate these ideas into the protocol stack 40 Natural continuation of DAWN MURI

41. What Remains? 41 Higher Layers Unicast Multicast Physical Layer Distributed Applications

42. What Remains? Much attention to  Moving bits between nodes in the network throughput delay, jitter packet loss  Cross layer ~ Layers 1-2-3 42 Higher Layers Unicast Multicast Physical Layer Distributed Applications

43. What Remains?  Not as much attention to semantics of distributed applications  How to exploit application-awareness ? 43 Higher Layers Unicast Multicast Physical Layer Distributed Applications

44. 44 Higher Layers Unicast Multicast Physical Layer Distributed Applications Distributed Primitives Wireless Network-Aware Distributed Primitives

45. Example primitives:  Ordered group communication  Consensus  Aggregation  Synchronization  Coordination 45 Higher Layers Unicast Multicast Physical Layer Distributed Applications Distributed Primitives Wireless Network-Aware Distributed Primitives

46. Example primitives:  Ordered group communication  Consensus  Aggregation  Synchronization  Coordination Network-awareness  Wireless capacity region  Diversity  Broadcast capability  Energy constraints 46 Higher Layers Unicast Multicast Physical Layer Distributed Applications Distributed Primitives Wireless Network-Aware Distributed Primitives

47. Past Work on Middleware  Similar motivation  But optimized for wired networks with high capacity and more benign characteristics 47

48. Wireless Network-Aware Distributed Primitives  Wired algorithms not efficient  Do not exploit wireless capabilities Many (new) fundamental problems open 48

49. Distributed Algorithms & Networking  Overlapping scope  But cultures differ 49 Communications / Networking Distributed Algorithms

50. 50 Distributed Algorithms Black box networks Emphasis on order complexity Emphasis on “exact” performance metrics Constants matter Communications / Networking

51. 51 Distributed Algorithms Black box networks Emphasis on order complexity Emphasis on “exact” performance metrics Constants matter Information transfer (typically “raw” info) Communications / Networking

52. 52 Distributed Algorithms Computation affects communication Emphasis on “exact” performance metrics Constants matter Information transfer (typically “raw” info) Communications / Networking Black box networks Emphasis on order complexity

53. Picture from Wikipedia Beneficial to bring together researchers in wireless networking & distributed algorithms Wireless Network-Aware Distributed Primitives

54. 54 Thanks!

55. 55 Thanks!

56. 56 Thanks!

57. 57 Thanks!

58. Scheduling Example A B C PROB E ACK DATA PROB E ACK DATA PROBE ACK DATA PROB E Acces s by a A Acces s by a B Acces s by p B Sensed busy by Link A & C Preempted by Link B Sensed idle by Link A & C Preempted by Link A & C Conflict graph for links A, B, C