Time Reversal for wireless communications

1. Time Reversal for wireless communications Persefoni Kyritsi PhD class on Adaptive Antennas Aalborg, Dec’04

2. Outline • Background • Convolution, Correlation • Beam forming in narrowband systems • (pre-)Equalization in wideband systems • Fundamentals of time reversal • Time domain • Frequency domain • Experimental demonstration • Applications • Opportunities for generalized time-reversal

3. Convolution and Correlation • Convolution • Correlation Properties • Frequency domain?

4. Beam forming in narrowband systems • One antenna: Where is the power going? • Many antennas: Where is the power going? a1 aM

5. Array pattern • Array pattern = (Element pattern) x (Array factor) • Array factor definition • How does it simplify for linear arrays? ULAs?

6. How communications work h(t) n(t) x(t) y(t) y(t) = h(t)  x(t) + n(t) • x(t): Transmitted signal • y(t): Received signal • h(t): Channel transfer function • n(t): Additive white Gaussian noise • : Pulse shaping function

7. 1 0 1 0 1 1 1 0 0 0 Effect of delayed copies

8. Wideband systems • How do we define wideband systems? Delay spread >> Symbol Time • Definition of the delay spread • What’s the picture in the frequency domain?

9. Wideband vs. Narrowband • Is it good or bad to have to deal with a wideband system? +: Diversity - : Inter-symbol interference (ISI) • What do we do at the receiver? Multi-carrier techniques (eg OFDM) Equalization (linear and non-linear)

10. Fundamentals of TR • Applicable in channels with LARGE delays spread (ds x bw > 20)

11. Historical background Ultrasound and underwater sound • Spatial focusing (w/o communications) in the last 15 years: • ultrasound (Fink, Paris), • underwater sound (Kuperman, UCSD). • Theory for spatial focusing in TR in random media • Jackson and Dowling (1990+), • Fink (1995+), • Kuperman, • Stanford Math Group (2002). • TR communication schemes demonstrated by • Kuperman (underwater sound, 2002), • Rouse. (passive underwater sound, 2001), • Fink (ultrasound, 2003, and EM, 2004), • Larazza (underwater sound, 2002). Space focusing and time compression of signals seen.

12. (t) TX TX Time Reversal: Time domain Phase 1: The transmitter learns the channel impulse response Phase 2: Each transmitter applies a filter and sends data (same data stream from all the elements)

13. x(t) Time reversal: Frequency domain

14. Why TR? • Benefits • Temporal focusing • Spatial focusing • Channel hardening

15. Temporal focusing • Delay spread is a fundamental limitation • irreducible BER, receiver complexity • TR can reduce the perceived DS • DS reduction depends on: • the number of transmitters NTX • transmit correlation

16. Spatial focusing with TR r r+d Interference (IF): At the sampling time:

17. Demonstration of MISO TR (au)

18. Demonstration of SISO TR (au)

19. Demonstration of MISO TR (au)

20. Experimental demonstration of TR • TR can achieve delay spread reduction and spatial focusing. • Exp 1: TR for fixed wireless applications (FWA)/ Temporal focusing study • Exp 2: TR in a WLAN scenario/ Spatial focusing study • Exp 3: TR in a multi-user context/ Spatial focusing study

21. x(t) Exp 1: MISO TR to a single user

22. Advanced weighting schemes • TR with antenna weighting: • Weight selection algorithms

23. FWA: Measurement equipment • Carrier frequency: 5 GHz • Transmitted power: PT = 100mW • 3dB bandwidth = 25MHz • 8 element uniform linear arrays (ULA) • Spacing: s = /2 • Vertical polarization • Vertical (V) or Horizontal (H) orientation

24. 29 floors (5, 19, 28) Balconies SW & NE 28 floors (19, 29) 7 floors (roof) FWA: Measument locations

25. Clear to cluttered Cluttered to clear Classification of MISO situations

26. Delay spread reduction: heq/h

27. Explanation: The shower curtain effect Psycho (1960)

28. Exp 2: TR in WLAN scenario • Range (O(km) vs O(10m)) • Delay spread (O(sec) vs. O(100nsec)) • Angular spread (O(60°) vs. O(360°)) • Delay spread reduction is not significant in WLAN scenarios • We are interested in and expect a lot of spatial focusing

29. 802.11n Channel model • SISO channel models (Medbo ‘98): Tap delay line model for various env’ts • MIMO channel models (Erceg et al ’03): Correlation-based model Clustering in • Time (Saleh-Valenzuela) • Angle (AoA and AoD)

30. AoA1 AoA2 AoA3 From SISO to MIMO SISO channel MIMO channel

31. 802.11n MIMO channel models • DS between 15ns and 150ns (BW802.11=20MHz, BWmodel=100MHz) • Each tap is associated with: • Number of clusters • Mean angle of arrival (per cluster) • Angular spread (per cluster) • Also known: • Doppler spectrum • Power roll-off law • Ricean distribution up to distance dMAX (K-factor)

32. Notation • Power on each tap • Correlation properties of each tap

33. Capacity

34. Interference for SISO TR

35. MISO spatial focusing (NTX=2)

36. Exp 3: Spatial focusing in MISO TR to multiple users • Each receiving antenna represents one of the Nr users

37. Interference calculation • Signal on target user • Interference from other users • The SIR

38. BS Reminder: The near-far problem U1 U2

39. Power control scenarios • The scaling factors normalize so that the total transmitted power is kept constant • Additional constraints • No power control across users • Simple power control across users

40. BS d Multi-user operation • Nu=2 • Antennas of 2 different terminals at locations along the route separated by distance d Measurement route

41. Measurements • fc=2.14GHz, • BW > 7MHz • 2 measurement routes of l=1km • Transmitter • 8 TX antennas • htx=20m (Balcony on 5th floor) • Receiver • On a trolley pulled by a van • Velocity 20-40km/hr • 4 RX antennas: A1, A2, B1, B2 at the four corners

42. Results for NR=2, multi-user With power control Without power control

43. Applications of time reversal • Cable replacement • Military • Sensor networks • Other ???

44. What if we don’t do exactly time reversal? • Target: Channels with large delay spread bandwidth products • Why are we interested in such channels? • Why not exactly TR?

45. Desirable features How is each of the following affected in HDB channels? • Spectral efficiency • Coverage • Reliability • Channel estimation • Signaling overhead • Low probability of intercept

46. Spectral efficiency in HDB channels • How to interpret spectral efficiency: (a) a single user (b) multiple users within the same cell (c) multiple cells HDB cannot improve capacity (open question for the multi-user case) In the MU case, HDB provides frequency diversity. CSIMO=CMISO(if CSI is available at Tx), but the SIMO channel does not have SF.

47. Coverage in HDB channels • How to interpret coverage: (a) SNR (b) fade margins SF does not buy coverage. HDB only buys coverage through diversity. Trade-off: coverage vs. transmission rate.

48. Reliability in HDB channels • How to interpret reliability Measure on the statistics of the link HDB helps, but there is no benefit from SF. Tx processing does not gain over Rx processing.

49. Channel estimation in HDB channels • How to interpret channel estimation: The receiver/ transmitter needs to know the channel in order to perform the decoding/ pre-coding. For the same amount of power, you have to estimate a lot more parameters in a HDB channel than in a non-HDB channel. This is problematic, especially for the weaker taps. Sol: Iterative TR?

50. Signaling overhead in HDB channels Signaling overhead Current systems have about 25-30 % signaling overhead, which eats up spectral effciency. Iterative TR would not be worth its cost in delay.