MegaMIMO: Scaling Wireless Throughput with the Number of Users. Hariharan Rahul, Swarun Kumar and Dina Katabi

Transcription

1 MegaMIMO: Scaling Wireless Throughput with the Number of Users Hariharan Rahul, Swarun Kumar and Dina Katabi

2 There is a Looming Wireless Capacity Crunch Given the trends in the growth of wireless demand, and based on current technology, the FCC projects that the US will face a spectrum shortfall in 213. The iphone 4 demo failed at Steve Jobs s keynote due to wireless congestion. Jobs s reaction: If you want to see the demos, shut off your laptops, turn off all these MiFi base stations, and put them on the floor, please.

3 MegaMIMO MegaMIMO alleviates the capacity crunch by transmitting more bits per unit of spectrum.

4 Today s Wireless Networks Ethernet Access Point 1 Access Point 2 Access Point 3 Interference! User 1 User 2 User 3 Access Points Can t Transmit Together in the Same Channel

5 MegaMIMO Ethernet Access Point 1 Access Point 2 Access Point 3 User 1 User 3 User 2 Interference from Interference from Interference from x 2 +x 3 x x 1 +x 3 1 +x 2 Data: x 1 survives Data: x Data: x 2 survives 3 survives All Access Points Can Transmit Simultaneously in the Same Channel

6 MegaMIMO Ethernet Access Point 1 Access Point 2 Access Point 3 User 1 User 3 User 2 Interference from Interference from Interference from Enables x 2 +x 3 senders xto 1 +x 3 transmit together x 1 +x 2 Data: x 1 survives Data: x Data: x 2 survives 3 survives without interference All Access Points Can Transmit Simultaneously in the Same Channel

7 MegaMIMO = Distributed MIMO Distributed protocol for APs to act as a huge MIMO transmitter with sum of antennas Ethernet AP1 AP2 AP3 AP1 User 1 User 2 User 3 User 1 1 APs 1x higher throughput

8 Diving Into The Details

9 Transmitting Without Interference AP 1 AP 2 Wants x 1 Receives y 1 Wants x 2 Receives y 2 y 1 = d 1 x 1 +. x 2 Cli 1 Cli 2 y 2 =. x 1 + d 2 x 2 y 1 y 2 = d 1 d 2 x 1 x 2

10 Transmitting Without Interference AP 1 AP 2 Wants x 1 Wants x 2 Receives y 1 Receives y 2 Cli 1 Cli 2 y 1 = d 1 x 1 +. x 2 y 2 =. x 1 + d 2 x 2 y 1 y 2 = D x 1 x 2 Diagonal

11 Transmitting Without Interference AP 1 AP 2 Wants x 1 Wants x 2 Receives y 1 Receives y 2 Cli 1 Cli 2 Diagonal Matrix y 1 = d 1 x 1 Non-Interference +. x 2 y 2 =. x 1 + d 2 x 2 y 1 Goal: Make the effective channel matrix = y D 2 diagonal Diagonal x 1 x 2

12 On-Chip MIMO All nodes are synchronized in time to within nanoseconds of each other. Oscillators at all nodes have exactly the same frequency, i.e., no frequency offset.

13 On-Chip MIMO Sends x 1 AP Sends x 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 y 1 = h 11 x 1 + h 12 x 2 y 2 = h 21 x 1 + h 22 x 2 y 1 y 2 = h 11 h 12 h 21 h 22 x 1 x 2 Non-diagonal Matrix Interference

14 On-Chip MIMO Sends x 1 AP Sends x 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 y 1 = h 11 x 1 + h 12 x 2 y 2 = h 21 x 1 + h 22 x 2 y 1 y 2 = h 11 h 12 h 21 h 22 x 1 x 2

15 On-Chip MIMO Sends s 1 AP Sends s 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 y 1 = h 11 s 1 + h 12 s 2 y 2 = h 21 s 1 + h 22 s 2 y 1 y 2 = h 11 h 12 h 21 h 22 s 1 s 2

16 On-Chip MIMO Sends s 1 AP Sends s 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 y 1 = h 11 s 1 + h 12 s 2 y 2 = h 21 s 1 + h 22 s 2 y 1 y 2 = H s 1 s 2

17 Making Effective Channel Matrix Diagonal Sends s 1 AP Sends s 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 y 1 = h 11 s 1 + h 12 s 2 y 2 = h 21 s 1 + h 22 s 2 y 1 y 2 = H s 1 s 2

18 Making Effective Channel Matrix Diagonal Sends s 1 AP Sends s 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 x 1 y 1 = h 11 s 1 + h 12 s 2 y 2 = h 21 s 1 + h 22 s 2 H -1 x 2 y 1 y 2 = H s 1 s 2

19 Making Effective Channel Matrix Diagonal Sends s 1 AP Sends s 2 h 11 h12 h 21 h 22 y 1 Cli 1 y 2 Cli 2 y 1 = h 11 s 1 + h 12 s 2 y 2 = h 21 s 1 + h 22 s 2 y 1 y 2 = H H -1 x 1 x 2 Effective channel is diagonal

20 Beamforming System Description Channel Measurement: AP1 and AP2 measure channels to clients Clients report measured channels back to APs Data Transmission: Packets are forwarded to both APs Each AP computes its beamformed signal s i using the equation s = H -1 x Clients 1 and 2 decode x 1 and x 2 independently

21 Distributed Transmitters Nodes are not synchronized in time. We use SourceSync to synchronize senders within 1s of ns (SIGCOMM 21) Works for OFDM based systems like Wi-Fi, LTE etc. Oscillators are not synchronized and have frequency offsets relative to each other.

22 MegaMIMO First wireless network that can scale network throughput with the number of transmitters Algorithm for phase synchronization across multiple independent transmitters Demonstrated in a wireless testbed implementation

23 What Happens with Independent Oscillators? AP 1 AP 2 h 11 h12 h 21 h 22 Cli 1 Cli 2 h 11 h 12 h 21 h 22

24 What Happens with Independent Oscillators? ω T1 AP 1 AP 2 h 11 h12 h 21 h 22 ω R1 Cli 1 Cli 2 h 11 e j(ω T1 - ω R1 )t h 12 h 21 h 22

25 What Happens with Independent Oscillators? ω T1 AP 1 AP 2 h 11 h12 h 21 h 22 ω R1 Cli 1 Cli 2 h 11 e j(ω T1 - ω R1 )t h 12 h 21 h 22

26 What Happens with Independent Oscillators? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 Cli 1 Cli 2 h 11 e j(ω - ω )t h 12 T1 R1 e j(ω - ω )t T2 R1 h 21 h 22

27 What Happens with Independent Oscillators? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 Cli 1 Cli 2 h 11 e j(ω - ω )t h 12 T1 R1 e j(ω - ω )t T2 R1 h 21 h 22

28 What Happens with Independent Oscillators? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 ω R2 Cli 1 Cli 2 h 11 e j(ω - ω )t h 12 T1 R1 e j(ω - ω )t T2 R1 h 21 ej(ω - ω )t h 22 T1 R2 e j(ω - ω )t T2 R2

29 What Happens with Independent Oscillators? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 ω R2 Cli 1 Cli 2 h 11 e j(ω - ω )t h 12 h 21 T1 R1 e ej(ω - ω )t h 22 T1 R2 e j(ω - ω )t T2 R1 j(ω - ω )t T2 R2 Time Varying

30 Channel is Time Varying ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 ω R2 Cli 1 Cli 2 H(t)

31 Does Traditional Beamforming Still Work? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 ω R2 Cli 1 Cli 2 y 1 (t) y 2 (t) = H(t) s 1 (t) s 2 (t)

32 Does Traditional Beamforming Still Work? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 ω R2 Cli 1 Cli 2 y 1 (t) y 2 (t) = H(t) H -1 x 1 (t) x 2 (t) Not Diagonal

33 Does Traditional Beamforming Still Work? ω T1 AP 1 ω T2 AP 2 h 11 h12 h 21 h 22 ω R1 ω R2 Cli 1 Cli 2 Beamforming does not work y 1 (t) y 2 (t) = H(t) H -1 x 1 (t) x 2 (t) Not Diagonal

34 Challenge Channel is Rapidly Time Varying Relative Channel Phases of Transmitted Signals Changes Rapidly With Time Prevents Beamforming

35 Distributed Phase Synchronization High Level Intuition: Pick one AP as the lead All other APs are slaves Imitate the behavior of the lead AP by fixing the rotation of their oscillator relative to the lead.

36 Decomposing H(t) h 11 e j(ω - ω )t h 12 T1 R1 e j(ω - ω )t T2 R1 h 21 ej(ω - ω )t h 22 T1 R2 e j(ω - ω )t T2 R2 e -jω R1 t h 11 e j(ω )t h 12 T1 e j(ω )t T2 e -jω R2 t h 21 ej(ω )t h 22 T1 e j(ω )t T2

37 Decomposing H(t) e -jω R1 t h 11 e j(ω )t h 12 T1 e j(ω )t T2 e -jω R2 t h 21 ej(ω )t h 22 T1 e j(ω )t T2

38 Decomposing H(t) e -jω R1 t h 11 h 12 e jω T1 t e -jω R2 t h 21 h 22 e jω T2 t

39 Decomposing H(t) e -jω R1 t h 11 h 12 e jω T1 t e -jω R2 t h 21 h 22 e jω T2 t

40 Decomposing H(t) e -jω R1 t e jω T1 t H e -jω R2 t e jω T2 t Diagonal Devices cannot track their own oscillator phases

41 Decomposing H(t) e -jω R1 t e -jω R2 t H e jω e jω T1 t e -jω T1 t T1 t e jω T2 t

42 Decomposing H(t) e j(ω - ω )t T1 R1 H 1 e j(ω - ω )t T1 R2 e j(ω - ω )t T2 T1 R(t) T(t) Depends only on transmitters

43 Decomposing H(t) e j(ω - ω )t T1 R1 H 1 e j(ω - ω )t T1 R2 e j(ω - ω )t T2 T1 R(t) T(t) H(t) = R(t).H.T(t)

44 Beamforming with Different Oscillators y 1 (t) y 2 (t) = R(t).H.T(t) H(t) s 1 (t) s 2 (t) s 1 (t) s 2 (t) = T(t) -1 H -1 x 1 (t) x 2 (t)

45 Beamforming with Different Oscillators y 1 (t) y H(t) 2 (t) = R(t).H.T(t) T(t) -1 H -1 Diagonal x 1 (t) x 2 (t) s 1 (t) s 2 (t) = T(t) -1 H -1 x 1 (t) x 2 (t)

46 Transmitter Compensation 1 T(t) = e j(ω - ω )t T2 T1

47 Transmitter Compensation 1 T(t) -1 = e -j(ω - ω )t Slave AP imitates lead by multiplying each sample by oscillator rotation relative to lead Requires only local information Fully distributed T2 T1

48 Measuring Phase Offset Multiply frequency offset by elapsed time Requires very accurate estimation of frequency offset Error of 25 Hz (1 parts per BILLION) changes complete alignment to complete misalignment in 2 ms. Need to keep resynchronizing to avoid error accumulation

49 Resynchronization AP 1 h 2 lead AP 2 Cli 1 Cli 2 h 2 lead (t) = h 2 lead e j(ω - ω )t Directly compute phase at each slave by measuring channel from lead T2 T1

50 Resynchronization AP 1 AP 2 Sync Data Lead AP: Prefixes data transmission with synchronization header

51 Resynchronization AP 1 AP 2 Sync Data Slave AP: Receives Synchronization Header Corrects for change in channel phase from lead Transmits data

52 Receiver Compensation y 1 (t) y H(t) 2 (t) = R(t).H.T(t) s 1 (t) s 2 (t)

53 Receiver Compensation y 1 (t) y H(t) 2 (t) = R(t).H.T(t) T(t) -1 H -1 x 1 (t) x 2 (t) y 1 (t) y 2 (t) = x 1 (t) R(t) -1 R(t) x 2 (t)

54 Receiver Compensation R(t) -1 = e -j(ω - ω )t T1 R1 e -j(ω - ω )t T1 R2

55 Receiver Compensation R(t) -1 = e -j(ω - ω )t T1 R1 e -j(ω - ω )t T1 R2 Receiver does what it does today correct for oscillator offset from lead

56 Enhancements Decoupling channel measurement across different clients Using MegaMIMO for diversity Compatibility with off-the-shelf 82.11n cards Described in paper

57 Performance

58 Implementation Implemented in USRP2 2.4 GHz center frequency OFDM with 1 MHz bandwidth 1 software radios acting as APs, all in the same frequency 1 software radios acting as clients

59 Testbed

60 Does MegaMIMO Scale Throughput with the Number of Users? Fix a number of users, say N Pick N AP locations Pick N client locations Vary N from 1 to 1 Compared Schemes: MegaMIMO

61 Total Throughput [Mb/s] Does MegaMIMO Scale Throughput with the Number of Users? Number of APs on Same Channel

62 Total Throughput [Mb/s] Does MegaMIMO Scale Throughput with the Number of Users? Number of APs on Same Channel

63 Total Throughput [Mb/s] Does MegaMIMO Scale Throughput with the Number of Users? MegaMIMO Number of APs on Same Channel 1x 1x throughput gain over existing Wi-Fi

64 What are MegaMIMO s Scaling Limits? Theoretical Practical N log SNR Can Scale Indefinitely with N Errors in H and phase synchronization affect accuracy of beamforming

65 What are MegaMIMO s Scaling Limits? N APs transmit to N users, N = Perform MegaMIMO as before, but with a zero signal for some client (i.e. null at that client) Phase Alignment is Accurate Received signal at noise floor ( db) Inaccuracy in Phase Alignment Received signal higher than noise floor (> db)

66 Interference to Noise Ratio (db) What are MegaMIMO s Scaling Limits? 1,8 1,6 1,4 1,2 1,8,6,4, Number of APs on Same Channel

67 Interference to Noise Ratio (db) What are MegaMIMO s Scaling Limits? 1,8 1,6 1,4 1,2 1,8,6,4,2 Interference to Noise Ratio ~ 1.5 db Number even of APs at 1 on Same userschannel

68 Compatibility with 82.11n 82.11n over 2 MHz Bandwidth Two 2-antenna USRPs acting as APs Two 2-antenna 82.11n clients Througput gain of MegaMIMO over 82.11n Demonstrates Compatibility with 82.11n clients Compatibility with MIMO APs and clients

69 Fraction of Runs Compatibility with 82.11n 1,9,8,7,6,5,4,3,2,1 1,65 1,7 1,75 1,8 1,85 1,9 1,95 2 Throughput Gain

70 Fraction of Runs Compatibility with 82.11n 1,9,8,7,6,5,4,3,2,1 1.8x 1,65 1,7 1,75 1,8 1,85 1,9 1,95 2 Throughput Gain

71 Fraction of Runs Compatibility with 82.11n 1,9,8,7,6,5,4,3,2,1 Median Gain of 1.8x with 2 receivers 1.8x 1,65 1,7 1,75 1,8 1,85 1,9 1,95 2 Throughput Gain

72 Related Work Theoretical Aeron et al., Simeone et al., Ozgur et al. (Distributed and Virtual MIMO) Empirical DIDO, Fraunhofer Network MIMO

73 Conclusion Wireless networks are facing a spectrum crunch MegaMIMO enables multiple independent transmitters to transmit to independent receivers in the same frequency bands Distributed and accurate phase synchronization Can enable wide body of theoretical work lattice coding, noisy network coding, distributed superposition coding, dirty paper coding