1. 1 Order Matters: Interference-Aware Transmission Reordering in Wireless Networks

2. 2 Wireless Networks Interference Limited Packet decoded successfully  When interference substantially lower  Else, collision Collision IEEE 802.11

3. 3 Phy Layer Capture Concurrent transmissions may not necessarily cause collision  Possible to decode the frame with higher SINR  As long as receiver not “locked” onto interference  Known as PHY layer capture What is locking onto a signal? What is locking onto a signal?

4. 4 Implications of Capture When stronger signal is of interest, AND Arrives within PLCP window  Concurrency feasible PLCP

5. 5 Implications of Capture When stronger signal is of interest, AND Arrives within PLCP window  Concurrency feasible However, PLCP duration small  Probability of precise timing also small millisecond 20 us

6. 6 Capture and MIM Message in Message (MIM)  Strong frame arrives after preamble of interfering frame  Receiver locked onto interference by then, and decoding  However, continues searching for another preamble  Strong message can be extracted while in another message

7. 7 Caveats Recognizing arrival of new preamble requires new preamble to be have higher SINR Only then correlation shows a high value

8. 8 SINR for MIM a function of relative arrival order and timing SINR Requirements [Lucent NIC] 4 dB 10 dB

9. 9 Order Matters Some signal-arrival orders will permit concurrency  Productive But the reverse order may cause collision  Unproductive Example …

10. 10 MIM Aware Scheduling AP1 must start first, followed by staggered transmission from AP2 -- concurrency feasible AP1 must start first, followed by staggered transmission from AP2 -- concurrency feasible 10 dB 5 dB

11. 11 MIM Aware Scheduling AP1 must start first, followed by staggered transmission from AP2 -- concurrency feasible AP1 must start first, followed by staggered transmission from AP2 -- concurrency feasible 10 dB 5 dB In general, weaker transmission must start first, stronger receiver suppresses it, and extracts own signal In general, weaker transmission must start first, stronger receiver suppresses it, and extracts own signal

12. 12 MIM Aware Scheduling AP1 must start first, followed by staggered transmission from AP2 -- concurrency feasible AP1 must start first, followed by staggered transmission from AP2 -- concurrency feasible 10 dB 5 dB In general, weaker transmission must start first, stronger receiver suppresses it, and extracts own signal In general, weaker transmission must start first, stronger receiver suppresses it, and extracts own signal Observe that 802.11 does not enforce this order, hence will fail to exploit MIM capabilities Observe that 802.11 does not enforce this order, hence will fail to exploit MIM capabilities

13. 13 Problem Definition: Design an MIM-aware scheduling algorithm that reorders transmissions to augment concurrency What is the bound on improvement? How to cope with time-vaying channel? How to sustain fairness and starvation?

14. 14 Solution Space Shuffle  A centralized MIM-aware scheduling protocol for Enterprise wireless LANs (EWLAN) AP2 Controller AP1 AP3

15. 15 Solution Space Shuffle  A centralized MIM-aware scheduling protocol for Enterprise wireless LANs (EWLAN)  Why EWLAN? 1. Becoming popular in single-admin environments § Offices, warehouses, libraries 2. Understand MIM for centralized systems, then goto distributed § Need to walk before running AP2 Controller AP1 AP3

16. 16 Shuffle: 3 Main Components 1.Measuring Interference Relation: Rehearsal  Characterize interference map to identify MIM opportunity  Cope with time-varying channel conditions 2.Packet Scheduler  Use rehearsal outcome to schedule transmissions  Scheduling = Reordering and staggering  Protect from unfairness and starvation 3.Schedule Coordinator  Execute MIM-aware schedule  Cope with failures, retransmissions, and centralized bottleneck

17. 17 Main Assumptions Dominant download traffic  Upload handled through periodic “upload windows” Processing time and latencies  Powerful controller, thin APs  Wired backbone fast, but can become bottleneck Additive Interference  Total interference = sum of individual interferences

18. 18 Feasibility First What is the maximum improvement with Shuffle in finite network scenarios? Determine the optimal link selection, and their relative order of initiation, to achieve this bound Observe that graph coloring inapplicable

19. 19 Analysis Optimal MIM-aware link scheduling is NP-Hard  Proof: Reduction from Independent Set selection MIM scheduling is special case Set SL (Signal Last SINR)=  Relative order requires the optimal choice of links first. Hence, NP-Hard

20. 20 Integer Linear Program Use ILP to upper bound improvement  For a large number of finite-sized topologies

21. 21 CPLEX Results MIM comparison with non MIM  Substantial improvement feasible, worth researching

22. 22 Protocol Design Measuring Interference Relations: Rehearsal

23. 23 Rehearsal Central controller needs link conflict information  Graph coloring notion of conflict not applicable  Conflicts also change with time-varying channel Basic idea:  Controller orchestrates a rehearsal of transmissions  Clients and APs record RSSI values as instructed times  Recorded RSSI correlated at controller  Inteference graph generated

24. 24 Rehearsal At network initialization  APs and clients informed about time of transmissions  Each AP transmits sequence of probes at base rate  Clients transmit probes, piggybacks recorded RSSI values  At the end, APs forward gathered values to controller  Controller derives interference map using additive interference assumption

25. 25 Interference Map Pairwise interferences mapped  Controller populates table i j Interference from i to j Interferer Sniffer...

26. 26 Rehearsal Opportunistic rehearsal  Continuous rehearsal expensive  Utilize regular transmissions to piggyback overheard RSSI Coping with Fading  Convergence may take long with opportunistic  Handling loss will require immediate conflict information  Perform self-corrective rehearsal using data packets Schedule packets conservatively to also serve the rehearsal purpose

27. 27 Rehearsal MIM Scheduler (Optimal NP-Hard) MIM Scheduler (Optimal NP-Hard) i j Interference from i to j

28. 28 MIM-Aware Scheduler Scheduler operation:  Choose non-conflicting packets from queue  Determine their relative starting order + stagger durations  Dispatch batch to AP Scheduler goal:  Maximize batch size  Protect from starvation  Ensure high fairness

29. 29 Greedy Heuristics Basic greedy  Fix a queue lookahead size for scheduling (say L)  Controller takes in-order packets from FIFO queue  Packet j scheduled if no conflict with pkts already scheduled Conflict is a function of SL and SF thresholds  If conflict, packet j postponed for next batch No starvation, Good Fairness  Every batch, a packet progresses in queue  Head of the batch always transmitted O(n 2 )

30. 30 Greedy Heuristics Randomized Greedy  Perform basic greedy on randomized subsets of queue  Probability of choosing packets biased Earlier in the queue have higher probability  Choose largest batch among all solutions Least-Conflict Greedy  Compute packet score = # of pair-wise conflicts Score higher if pkt must start earlier, lower else  Sort packets based on score  Perform basic greedy on this sorted order  Incorporate aging for fairness/starvation O(n 2 logn) O(n 2 )

31. 31 Optimal Vs Greedy (variants)

32. 32 Rehearsal MIM Scheduler (Optimal NP-Hard) MIM Scheduler (Optimal NP-Hard) Schedule Coordinator (ReTx, Prefetch, Predict) Schedule Coordinator (ReTx, Prefetch, Predict)

33. 33 Schedule Coordinator Packets dispatched to APs  Time synchronized between APs and contollers  Pipeline Controller to AP, and AP to Client transmissions  APs transmit at specified time

34. 34 Schedule Coordinator ACK Transmission  Controller embed ACK schedule in Data Packet header  Clients follow schedule (MIM-aware)  AP forwards ACKs to controller (ACKs may have RSSI)  When no ACK, AP forwards NACK  Lost packets scheduled with highest priority

35. 35 Batch Selection C1 C4 C3 C1 C2 C1 Queue Batch

36. 36 Controller sends packets to APs C1 C4 C3 C1 C2 C1 Queue Batch

37. 37 APs/Clients Stagger transmissions C1C3C4 ACK Data Staggering Order: AP2-AP3-AP1 ACK Staggering Order: C4-C3-C1

38. 38 Coping with Fading Loss Time varying channel  Interference graph changes  Subsequent MIM scheduling can cause further failures Immediate corrective rehearsal  Controller identifies links suspected of fading  Schedules a packet batch only for these APs This is a partial rehearsal Packets are transmitted in serial order  APs and clients unaware, send Data and ACKs  Controller updates interference map from ACK RSSIs

39. 39 Pipelining Batches Batch - ACK - Batch inefficient  APs remain idle between batches (not negligible) Controller sends 2 batches to AP  AP sends batch 1 and receives ACKs  Batch 2 started, ACKs forwarded to controller in parallel  Controller processes ACKs and next batch in parallel  Controller schedules batch 3, sends to APs  AP finishes batch 2  Repeat …

40. 40 Testbed Evaluation Looks Familiar?

41. 41 Linux Laptops + Soekris + WiFi Device Driver Validating that order really matters

42. 42 Evaluation Shuffle performs better

43. 43 Ordering Not all ordering is same -- hence, random not enough

44. 44 Future Work MIM aware routing  Choose paths such that MIM is maximally activated Distributed Shuffle  We presented for EWLAN  What about residential WLANs, organic emergence? Can postambles be useful?  As opposed to Preamble (Hari Balkrishnan, NSDI 08)

45. 45 Conclusion Necessary to pay attention to PHY layer capabilities  Interference cancellation (its first steps) one example MIM is ability to extract frame of interest  Even under ongoing interference  Provided some (relative order, SINR) conditions hold Facilitating these conditions can enable MIM  Rich performance gains feasible MIM-aware link layer scheduling necessary

46. 46 Conclusion Shuffle - MIM scheduling for EWLANs  EWLANs proliferating, also foundation for distributed case NP-Hard problem  Bounds characterized through linear programming Greedy scheduling heuristics perform well  Performance close to optimal Evaluation on Linux testbed + Soekris boxes  Consistent improvement, even under fading and losses

47. 47 Questions?

48. 48 Interference Cancellation in Wireless LANs

49. 49 Successive Interference Cancellation (SIC) State of the art allows only one reception  The stronger one SIC enables a receiver to receive both signals  Stronger signal decoded and subtracted  Residual signal decoded from the residue

50. 50 SIC based WLANs Existing schemes require SINR >   Game of out-shouting each other SIC offers payoff if transmitter  Either out-shouts or whispers  Fundamental changes for protocol design

51. 51 MIM + SIC Ongoing work on GNU radios  SIC + MIM implementation can enable protocols

52. 52 Diving a Little More Modulation 101  How do you transmit a bit sequence?  Need to convert bits onto analog domain … convert back  Basic Idea: Change the properties of a signal (amplitude, frequency, phase) to reflect the bit that you are trying to convey Example: Shout loud for +1, whisper for 0 Shouting loud is a symbol for +1 … whispering is a symbol for 0

53. 53 Modulation 101 But humans can modulate their voice better  So why not shout/whisper at different levels? Each level a symbol -- each symbol carries multiple bits 11 10 01 00

54. 54 Modulation 101 But humans can modulate their voice better  So why not shout/whisper at different levels? Each level a symbol -- each symbol carries multiple bits 11 10 01 00 So if recevier samples at the right time (during peak or trough) then it can get a value. Since it knows the ranges for each symbol, it knows what symbol was received … hence, what bit sequence. So if recevier samples at the right time (during peak or trough) then it can get a value. Since it knows the ranges for each symbol, it knows what symbol was received … hence, what bit sequence.

55. 55 With Actual Signals More opportunity:  Modulate a Sin(.) and a Cos(.) signal with different bits  This is like 2D space Called constellation diagram  Send the sum of both as a single symbol  Receiver gets sum, and can extract both using a coherent demodulator

56. 56 Quadrature Amplitude Modulation (QAM) Mathematically

57. 57 16 QAM reception Receiver gets a dot  Computes nearest neighbor as the transmitted symbol  Hence, the bits are now decoded

58. 58 16 QAM reception Receiver gets a dot  Computes nearest neighbor as the transmitted symbol  Hence, the bits are now decoded Do you see why higher Data rate increases the Probability of error? Because, separation between Symbols become smaller Do you see why higher Data rate increases the Probability of error? Because, separation between Symbols become smaller

59. 59 Easier Said Than Done Lots of issues:  Rx is assumed to know the phase of transmitted signal So that Rx can sample at the right time  But difficult because signals getting reflected  Also, Rx’s frequency needs to be exactly same as Tx Recall PLCP  It helps in straightening these out

60. 60 So Then … How do You Do SIC? Basic Idea:  Let received combined signal be S’ … Stronger received signal be S1, weaker received signal be S2  Synchronize with the stronger signal By detecting PLCP  Demodulate by treating the weaker as interference Get the bits out  Now, model the stronger signal based on the bits (S1’) To see how it would look without the interference (S1’ != S1)  Now subtract: i.e., S2’ = S’ - S1’  Demodulate S2’ to get the bits out How

61. 61 Modeling a Symbol from Bits 00 01 10 11

62. 62 Modeling a Symbol from Bits 00 01 10 11 S’S’

63. 63 Modeling a Symbol from Bits 00 01 10 11 S’S’ S1 ’

64. 64 Modeling a Symbol from Bits 00 01 10 11 S’S’ S1 ’ -S1 ’

65. 65 Modeling a Symbol from Bits 00 01 10 11 S’S’ S1 ’ -S1 ’ S2 ’ = S ’ + (-S1 ’ ) Thus weaker signal is bit 11

66. 66 All Modeling, Subtracting in Software USRP (Universal Software Radio Peripheral)  Connected to laptop for doing processing  This paper demonstrated offline SIC All signals received, and procesing done after that

67. 67 ZigZag Decoding: Combating Hidden Terminals in Wireless Networks Shyamnath Gollakota and Dina Katabi MIT CSAIL SIGCOMM 2009

68. 68 Hidden Terminal Problem Leads to low utilization of bandwidth and unfairness in channel access RTS/CTS induced too much overhead Collided packets may still be decodable! Alice Bob AP X

69. 69 Basic idea of ZigZag Decoding Chunk 1 from user A from 1 st copy of collided packet can be decoded successfully  Subtract from 2 nd copy to decoded the Chunk 1 of user B Subtract from 1 st copy of collided packet to decode Chunk 2 from user A –Subtract from 2 nd copy of collided packet to decode Chunk 2 from user B

70. 70 Wait! What about Shannon Capacity? Requires retransmissions if collision occurs  No overhead if no collision R1 R2 TDMA

71. 71 Other alternatives CDMA  Incompatible with WLAN  Low efficiency in high SNR Successive interference cancellation (SIC)  Chunk == packet  Decode the strong signal first, subtract from the sum and then decode the weak signal  No need for retransmissions  Both transmitters need to transmit at a lower rate

72. 72 Patterns that ZigZag Applicable Both backward and forward decoding can be used

73. 73 Technical Barriers How do I know packets collide Matching collision happened? (P1, P2) and (P1’, P2’) Frequency offset between transmitter and receiver Sampling offset Inter-symbol interference What if errors occur in chunks Acknowledgement? } subtraction is non-trivial

74. 74 Evaluation 14-node GNURadio testbed  USRP with RFX2400 radio (2.4 GHz)  BPSK  Bit rate 500kbs  32-bit preamble  1500-byte payload, 32-bit CRC Deficiency in GNURadio  Cannot coordinate transmission and reception very closely  CSMA, ACK TransmitterReceiver Software

75. 75 Micro-benchmark

76. 76 Alice & Bob Bob’s location is fixed, Alice moves closer to the base-station

77. 77 Impact of SNR on BER Alice & Bob at fixed and equal location Vary transmission power level

78. 78 Testbed Results Pick two senders randomly  10% hidden terminals, 10% partial, 80% perfect

79. 79 Three hidden terminals

80. 80 Conclusion ZigZag improves fairness & throughput Further research  Combination of analog network coding

81. 81 Questions?

82. 82 Preliminary on communication BPSK: 0 -> -1 1 -> 1 http://en.wikipedia.org/wiki/QPSK

83. 83 Collision Detection Preamble  Pseudo random number  Correlation with moving window  thresholding

84. 84 Matching collision Given (P1 + P2(  )) and (P1’, P2’(  ’)), how to determine that P1 = P’ and P2 = P2’’  Determine offset first  Correlation of P2(  ) and P2’(  ’)

85. 85 Decode matching collision Decode iteratively Re-encoding  Computing channel parameters Channel gain estimated from Frequency offset and sampling error 1) coarse estimation from previously successful reception 2) iterative estimation Inter-symbol interference: take the inverse of linear filter (for removal of ISI)

86. 86 Decode matching collision (cont’d) Re-encoding  Account for sampling error

87. 87 What about errors? Will errors in decoding have a cascading effect?  Error propagation dies out exponentially Error correction capability of modulation  Forward and backward decoding

88. 88 Acknowledgement Use as much synchronous acknowledgement as possible for backward compatibility

89. 89 Duke EWLAN Topology Client, AP placement traces used to feed Qualnet  Fading models from Qualnet  Only 4 topologies shown in graph

90. 90 Increasing AP Density Shuffle throughput higher in denser conditions  Greater scope to “squeeze in” transmissions in space

91. 91 Latency Improves Latency increases due to higher concurrency  As well as from TDMA scheduling

92. 92 Under Channel Fading Corrective rehearsal effective to cope with fading  We observed loss fraction of 12% under Ricean.

93. 93 Ongoing Work Integrating upload traffic  Proposing upload windows  Can be opportunistically used for download (ZMAC)  Can be used to accommodate client joins, departure Interference from external networks affect schedule  Need to treat border APs separately Interference cancellation may decode both signals  More powerful than MIM, hence, new MAC necessary  We are investigating possibilities through GNU radio

94. 94 Today’s Menu Spotlight Shuffle Micro-Blog Mingle

95. 95 Micro-Blog: A Virtual Information Telescope using Mobile Phones and Social Participation

96. 96 Mobile Phones = Powerful Sensors Next Generation Mobile Phones  Variety of embedded sensors - Cameras, mic., accelerometer, health monitor, RFID reader 3 Billion active phones  2009 - phone sales will surpass computers  Convergent device accepted technologically, socially

97. 97 Vision Envision each mobile phone as a virtual lens Imagine an Information Telescope over 3 billion lenses Enabling you to zoom in and perceive any part of the world through the eyes and ears of these phones And even querying them in real time, with automatic, social, or participatory replies

98. 98 Micro-Blog Virtual Telescope Internet, Cellular Networks Internet, Cellular Networks Visualization Service Web Service Sensors Phones People Physical Space

99. 99 Prototype: From Japan

100. 100 Prototype: From Sydney

101. 101 Post-Its in the Air Information superimposed on virtual space  Google maps, Microsoft SensorMap, etc. Feasible to superimpose on physical space  As if sticky notes floating in the air  Downloadable into mobile phones … Prototype for Duke campus

102. 102 Micro-Blog [mobisys2008] Project Website  http://microblog.ee.duke.edu Project live at  http://152.3.193.194/microblog/dev7/microblog.php

103. 103 So, where exactly is the research here ???!!**

104. 104 Many Challenges Energy-Aware Localization [mobisys08_poster]  GPS offers 7 hours battery, but hi accuracy  Alternates tradeoff accuracy for energy

105. 105 Many Challenges Location Privacy  Users do not want to reveal location  Partial location important for contextual info. Incentives  No reason for user to participate  Designing incentive schemes Too much information entering the system  Information distillation critical … many many more

106. 106 Thanks a lot for your time and patience SyNRG Homepage: http://synrg.ee.duke.edu

107. 107 Analogy Imagine a graphic equalizer  How do you know what setting will play the song best?  If each song had a “known tune” preceding it You could set the graphic equalizer based on the tune Then listen to the song well Analogous to “locking” on to the song

108. 108 Similarly … Payload in data frame preceded with PLCP  PLCP like pilot signal  Receiver uses for synchronization/correction with Tx  During synchronization, Rx susceptible to distraction  Once synchronized, following bits can be well decoded However, if strong interference, then collision Back

109. 109 Backup Slides

110. 110

111. 111 The Menu Spotlight Shuffle Micro-Blog Mingle

112. 112 Optimal link schedule w, w/o MIM: shows potential gain with MIM-awareness Optimal link schedule w, w/o MIM: shows potential gain with MIM-awareness Integer Programming

113. 113 Vision Design a (software) information telescope to zoom into a any part of the world, and view it through virtual lenses located there Design a (software) information telescope to zoom into a any part of the world, and view it through virtual lenses located there Query the lenses in real time Incentivize participatory sensing Enable automatic sensing Incentivize participatory sensing Enable automatic sensing

114. 114 Our Research PHY MAC / Link Network Transport Security Application Incentives Channel fluctuations Spatial Reuse Mobility Energy Savings Eavesdropping Loss Discrimination Privacy Ubiquitous Services Interference Mgmt. What can be enabled (bottom up) What can be enabled (bottom up) What are the visions (top down) What are the visions (top down)

115. 115 Shuffle: 3 Main Components 1.Measuring interference relationship - Rehearsal -Controller orchestrates rehearsal -Each node measures interference map from all others -Result is a network-wide interference map 2.Scheduler determines links and order of transmission -From the interference map -Scheduling NP-Hard --> approximation algorithms -Packets scheduled in batches 3.Schedule manager executes schedule -Copes with failures, fading, mobility -Performs pre-fetching, speculation, prediction

116. 116 Collection of wireless hosts  Relay packets on behalf of each other  Together form an arbitrary topology  May be connected to wired infrastructure 2 reasons to prefer multihop  Capacity and Power constraint Wireless Multihop Networks B A C D

117. 117 MIM Scheduling 0/1 Solution to ILP satisfies Hence, IP solution is the time-ordered schedule

118. 118 2 Key Architectures  Single hop networks  Multi-hop networks

119. 119 Wireless Single Hop Networks Cellular Networks Distributed WLANs Centralized Enterprise WLANs

120. 120 Collection of wireless hosts  Relay packets on behalf of each other  Together form an arbitrary topology  May be connected to wired infrastructure 2 reasons to prefer multihop  Capacity and Power constraint Wireless Multihop Networks

121. 121 The Context The edge of the internet becoming wireless  167,000 hotspots by 2008 end [GartnerSurvey06]  75 million user base  Mesh network extensions to rural regions Many Motivations to get unplugged  Unrestricted mobility  Significantly lower deployment/maintenance cost  Ease of use

122. 122 Proliferating Applications and Technologies When combined in synergy … Mesh Networks Sensor Networks Social Communities Mobile Networks Ad Hoc Networks RFID Tracking Personal Area Networks Hybrid Networks Mobile Blogging Location Services Smart Clothes Information Mapping Gaming

123. 123 The Key Intuition Ability to Decode = Ability to Cancel In other words, knowing the structure of interference, helps in coping with it In other words, Stronger, decipherable interference better than weak, undecipherable ones

124. 124 Theory Vs Practice Theoretically, cancellation feasible In practice, perfect cancellation difficult  Suppression beneficial  Along with some help from higher SINR requirements Suppression + Higher SINR = Concurrency