1. Achieving Spectrum Efficiency Lili Qiu University of Texas at Austin 1

2. Motivation Explosive increasing user traffic Spectrum crisis [NewYorkTimes12, CNET12, …] Urgent need of new technologies to dramatically increase spectrum efficiency 2

3. Our Work Spectrum sharing – Develop an efficient MAC for multiple nodes to efficiently share the medium Spectrum access – Develop and implement multi-point to multi- point MIMO 3

4. Our Work Spectrum sharing – Develop an efficient MAC for multiple nodes to share the medium Spectrum access – Develop and implement multi-point to multi- point MIMO 4

5. CRMA: Collision Resistant Multiple Access Joint work with Tianji Li, Mi Kyung Han, Apurv Bhartia, Eric Rozner, Yin Zhang, Brad Zarikoff ACM MobiCom 2011 5

6. Motivation Traditional MAC protocols avoid collisions – FDMA, TDMA, FTDMA, CSMA, … Avoid collisions  large overhead – FDMA: unoccupied channel and guard band – TDMA: global coordination and centralized scheduling – CSMA: carrier sense overhead, hidden terminals, exposed terminals 1500-byte frame: 29% for 802.11a (54Mbps) and 72% for 802.11n (600Mbps) TCP ACK: 77% overhead for 802.11a and 83% overhead for 802.11n –…–… 6

7. Collision Avoidance  Collision Resistance Let collisions happen naturally and decode collisions 7

8. Our Contributions New encoding/decoding to allow multiple signals transmitted on the same channel Collision resistant medium access protocol (CRMA) based on the encoding/decoding Evaluation to show CRMA is a promising direction for spectrum sharing 8

9. CRMA: An Illustrating Example 9 Channel 1 Channel 2 S1R1 S2 R2 Randomly pick a channel? - 50% collisions! Coordinate to avoid using the same channel? -Large overhead especially for lots of dynamic flows

10. CRMA: An Illustrating Example 10 Channel 1 Channel 2 Frame 1 Frame 2 Frame 1 Frame 2 S1R1 S2 R2

11. CRMA: An Illustrating Example 11 Channel 1 Channel 2 Frame 1 Frame 2 Frame 1 Frame 2 S1R1 S2 R2

12. CRMA: Research Questions 12 Channel 1 Channel 2 Frame 1 Frame 2 Frame 1 Frame 2 AB C D

13. CRMA: Research Questions 13 Channel 1 Channel 2 Frame 1 Frame 2 Frame 1 Frame 2 AB C D -What is the code c? -How do the sender and receiver agree on the code?

14. CRMA: Research Questions 14 Channel 1 Channel 2 Frame 1 Frame 2 Frame 1 Frame 2 AB C D - What is the code c? -How do the sender and receiver agree on the code? -How to decode transmissions? -How to handle decoding failures? -How to decode misaligned collisions? -How to limit # transmissions in a collision? -How to enhance spectrum utilization?

15. CRMA -What is the code c? -How do the sender and receiver agree on the code? -How to decode transmissions? -How to handle decoding failures? -How to decode misaligned collisions? -How to limit # transmissions in a collision? -How to enhance spectrum utilization? 15

16. Code Selection We use a binary code for simplicity – C(i,f)=1 if transmitter i uses channel f, otherwise 0 16 Channel 1 Channel 2 Channel 3

17. Code Selection We use a binary code for simplicity – C(i,f)=1 if transmitter i uses channel f, otherwise 0 17 Channel 1 Channel 2 Channel 3

18. Code Selection We use a binary code – c(i,f)=1 if transmitter i uses channel f, otherwise 0 18 Channel 1 Channel 2 Channel 3

19. Code Selection (Cont.) 19

20. CRMA -What is the code c? -How do the sender and receiver agree on the code? -How to decode transmissions? -How to handle decoding failures? -How to decode misaligned collisions? -How to limit # transmissions in a collision? -How to enhance spectrum utilization? 20

21. Code Establishment Using control channel – The sender and receiver negotiate the code on a separate control channel In band notification – Each frame has two PN sequences to denote sender and receiver IDs – A receiver correlates the received signal with its ID to determine if the frame is destined to itself with senders’ IDs to determine who send traffic – Correlation is close to 0 except when perfectly aligned with the IDs  works under collisions! – In-band processing but no need for control channel 21

22. CRMA -What is the code c? -How do the sender and receiver agree on the code? -How to decode transmissions? -How to handle decoding failures? -How to decode misaligned collisions? -How to limit # transmissions in a collision? -How to enhance spectrum utilization? 22

23. Decoding Transmissions Detect frame arrival and departure – Correlate the received signal with the preamble and postamble – Correlation is close to 0 except when perfectly aligned with preamble or postamble 23 Accurate preamble detection (e.g., false positive and false negative ratios are 0 when SINR=-2).

24. Decoding Transmissions (Cont.) 24

25. Handling Decoding Failures Use ACKs and retransmissions to enhance reliability ACKs are sent in the same way as data frames – Receiver sends an ACK on the same set of selected channels – Sender decodes the ACK by solving a linear system (as decoding data frame) 25

26. Problem of Misaligned Collisions 26 Symbol 1 Symbol 2 FFT window

27. Handling Misaligned Collisions 27 CP i-1 CP i CP i+1 CP i-1 CP i CP i+1 Symbol i-1 Symbol i+1 offset FTT window Cyclic prefix (CP) allows collided symbols fall in the same FFT window

28. Handling Misaligned Collisions 28 CP i-1 CP i CP i+1 CP i-1 CP i CP i+1 Symbol i-1 Symbol i+1 offset FTT window same

29. Other Design Components 29

30. Evaluation Methodology Testbed experiments show feasibility – Implement CRMA on top of the default OFDM implementation in USRP – 5 GHz, BPSK, 200 subcarriers, each 1.95KHz Qualnet simulations evaluate efficiency – Compare CRMA w/ and wo/ virtual flows, CSMA/CA (multiple channels), WiFi (one channel), random access – 1000-byte frames, 16 QAM, 20MHz total spectrum divided into 10 channels – 700 MHz for long distance, and 5 GHz for short distance networks 30

31. Testbed Experiments (Cont.) SINR (dB)High SINRLow SINR 025% 198%95% 398%95% 5100%98% 799%0.09% 31 Delivery rate of 1000-byte frames High decoding rate when 1 ≤ SINR ≤ 5, and degrades as SINR approaches 0 or too high. The latter could be improved by partial packet recovery.

32. Testbed Experiments (Cont.) 32 CRMA accurately decodes collisions up to 140 sample offsets.

33. Qualnet Simulation: Varying # flows in long distance networks 33 CRMA-VF > CRMA > other schemes.

34. Qualnet Simulation: Varying # flows in short distance networks 34 CRMA-VF > CRMA > other schemes.

35. Qualnet Simulation: Varying data rate 35 CRMA significantly out-performs the other schemes and its benefit increases with data rate.

36. Related Work Decoding collisions – Successive interference cancellation, ZigZag, analog network coding, … – CRMA: a MAC protocol based on ability to decode collisions CDMA – Synchronous CDMA: handful orthogonal codes and requires tight synchronization – Asynchronous CDMA: suffers Multiple Access Interference (MAI) Channel assignment and channel hopping – Try to avoid collisions – CRMA: a new perspective on spectrum sharing 36

37. Summary of CRMA CRMA: a new direction for spectrum sharing – A new encoding and decoding scheme – A new MAC protocol based on it – Experimental evaluation to show it can achieve high efficiency without fine-grained coordination Future work – Robust to channel estimation errors – Effectively support high data rate – More graceful degradation as # transmissions exceeds # channels 37

38. Our Work Spectrum sharing – Develop an efficient MAC for multiple nodes to share the medium Spectrum access – Develop and implement multi-point to multi- point MIMO 38

39. Multi-point to Multi-point MIMO in WLANs Joint work with Sangki Yun and Apurv Bhartia 39 Under submission

40. Overview Motivation – MIMO promises a dramatic capacity increase 802.11n, 802.11ac, … – But usually limited by # antennas at an AP – Multi-point to multi-point MIMO achieves a higher capacity and overcomes the limitations 40

41. Multi-point to Multi-point MIMO 41 AP1AP2APn Client1Client2Client n n concurrent downlink or uplink streams

42. Multi-point to Multi-point MIMO 42 AP1AP2APn Client1Client2Client n n concurrent downlink Is it feasible?

43. Our Contributions Demonstrate the feasibility and effectiveness of multi-point to multi- point MIMO on USRP and SORA Design multi-point to multi-point MIMO-aware MAC Extensions to support general traffic and network conditions 43

44. Our Contributions Demonstrate the feasibility and effectiveness of multi-point to multi- point MIMO on USRP and SORA Design multi-point to multi-point MIMO-aware MAC Extensions to support general traffic and network conditions 44

45. Point-to-Point MIMO 45 AP Client

46. Multi-point to Multi-point MIMO: Downlink 46 AP1AP2APn Client1Client2Client n n concurrent downlink

47. Multi-point to Multi-point MIMO in Downlink Clients can not cooperate APs perform joint precoding (e.g., zero- force beamforming) – Such that the combined precoded signals arriving at the clients can be modulated as usual – Precoding matrix W = H -1 – Received signal HWp=HH -1 p= p Is it feasible in practice? 47

48. Practical Challenges Each AP has its own clock  different carrier frequency offset (CFO) Challenges – Phase synchronization – Time synchronization 48

49. Phase Synchronization (I) 49

50. Phase Synchronization (II) 50

51. Phase Synchronization (III) 51

52. Distributed MIMO in Uplink 52 Share the received signals over the Ethernet Client 1 AP 2 AP 1 Client 2 APs share their received signals and jointly decode y 1 = h 11 p 1 + h 12 p 2 y 2 = h 21 p 1 + h 22 p 2

53. MAC Design Rate adaptation Support ACKs Deal with losses and collisions Schedule transmissions Limit Ethernet overhead Obtain channel estimation 53

54. MAC Design Rate adaptation Support ACKs Dealing with losses and collisions Scheduling transmissions Limiting Ethernet overhead Obtaining channel estimation 54

55. Rate Adaptation (I) Challenges – Receiver receives a combination of signals from all the transmitting APs – Per link SNR based rate adaptation does not work 55

56. Rate Adaptation (II) Error vector magnitude (EVM) based SNR – Distance between the received symbol and the closest constellation point – Incorporate frequency diversity by computing this metric for each subcarrier to derive BER and effective SNR 56

57. Support ACKs ACKs enjoy the same spatial multiplex in the reverse direction Downlink – Data: APs multiplex to clients via precoding – ACK: clients multiplex to APs and APs jointly decode Uplink – Data: clients multiplex to APs and APs jointly decode – ACK: APs multiplex to clients via precoding 57

58. Evaluation Implement downlink on USRP – SORA transmitter has random initial phase and makes it hard to support phase sync. Implement uplink on SORA – Both USRP and SORA support uplink and use SORA for higher capacity 58

59. Downlink Phase Misalignment 59 Median phase misalignment is 0.075 radian and reduces SNR by 0.4 dB.

60. Downlink Throughput 60 Downlink throughput almost linearly increases with # antennas across different APs or clients.

61. Uplink Throughput 61 Uplink throughput almost linearly increases with # antennas across different APs or clients.

62. Ran adaptation in Downlink 62 Achieves close to 96% throughput of best fixed rate.

63. Summary First step towards multi-point to multi- point MIMO This new transmission method opens up – New network optimization – New network management – New applications 63

64. Thank you! 64

65. Qualnet Simulation: Varying payload size 65 CRMA out-performs the other schemes and its benefit is larger for small packets.

66. Testbed Experiments (Cont.) 66 Phase shift correctly compensate for the offset signal.

67. Ethernet Client 1 Commander Client 2Client n

68. Frequency Synchronization 68

69. Multi-point to Multi-point MIMO in Downlink Clients can not cooperate APs perform joint precoding (e.g., zero- force beamforming) – Such that the combined precoded signals arriving at the clients can be modulated as usual – Precoding matrix W = H T (HH T ) -1 – Received signal HWx=HH T (HH T ) -1 x= x Is it feasible in practice? 69

70. Overview Motivation – MIMO promises a dramatic capacity increase 802.11n, 802.11ac, … – But usually limited by # antennas at an AP – Multi-point to multi-point MIMO achieves a higher capacity and overcomes the limitations State-of-art – Theory: significant work on distributed MIMO – Practice: not clear how well it works in reality 70