Improving Performance of Wireless Networks

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

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

3. Multi-Channel Environments Available spectrum Spectrum divided into channels 1 2 3 4 … c

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

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

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 S R D

8. 2 2 + 1 - 1 = = + 2 + 1 2,1,3 2,1,3 0,2,1 0,2,1 3,0,2 3,0,2 1,6,6 7,4,9 + b + c = a P1 P2 P3 a,b,c 3 0 + 0 + 2 + 2 + 1 = = P1 P1 P2 P2 P3 P3 0,2,1 3,0,2 2 + 1 + 3 = P1 P3 2,1,3 P2 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 S R D P1 2,1,3 P1 2,1,3 P2 0,2,1 7,4,9 P2 P3 3,0,2 1,6,6 P3

9. Opportunism versus Concurrency • For opportunistic scheme to work,nodes must be on the same channel • Reduces concurrency S R D

10. Trade-Off

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

12. “Opportunism-Aware” Channel Assignment C1 C2 A 0.75 0.25 C1 C1 0.5 0.9 S D 0.75 0.25 C2 C2 B C1 C2 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

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

15. Packet Size-Dependent Channel Selection

16. Channel Width • Typically, channels are assumed identical width • May benefit by varying channel widths 1 2 3 4 … c

17. Motivation Rate-independent MAC overhead L1 /R L1 bits DIFS Header DIFS Header L2 bits L2 /R T

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

19. Current Approach • Frame Aggregation (used in IEEE 802.11n) • Aggregate and send multiple packets in a single transmission opportunity L1 bits L2 bits L3 bits DIFS Header Multiple packets to amortize overhead overhead

20. Packet Size-Dependent Channel Widths • 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 withmultiple other nodes A

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

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

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 whenCS is imperfect ?

26. Throughput-Optimal Schedulers • A scheduler is throughput-optimal ifit can serve all schedulable traffic • Throughput-optimal scheduler byTassiulas-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 Lastα-duration of each time slot for carrier sense

31. Preemptive CSMA • Two access probabilities: ai and pi Carrier sense u(t): preemption x(t): transmission schedule Ci: set of conflict links of i ACK reception

32. Performance Analysis • Schedule evolution: discrete-time reversible Markov chain Stationary distribution • Cu : set of conflicting links of links in u • When pi = 1 - = 1exp{wi(qi)} exp{wi(qi)} -1exp{wi(qi)}

33. Throughput-Optimality • Preemptive CSMA is throughput-optimal • When access probabilities are • 0 < aLB ≤ ai ≤ aUB < 1 • pi = 1 - 1/exp{wi(qi)} where wi is a strict concave function with wi(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 ?

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 ofcross-layer protocol design

40. What Remains? • Physical layer community has also been making significant progress • Interference alignment • Cooperation • Security • Need to incorporate these ideas intothe protocol stack Natural continuationof DAWN MURI

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

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

43. What Remains? • Not as much attention to semantics ofdistributed applications • How to exploitapplication-awareness ? Distributed Applications Unicast Multicast Higher Layers Physical Layer

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

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

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

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

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

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

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