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Time Reversal for wireless communications

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Balconies SW & NE. 28 floors (19, 29) 7 floors (roof) ... htx=20m (Balcony on 5th floor) Receiver. On a trolley pulled by a van. Velocity 20-40km/hr ... – PowerPoint PPT presentation

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Title: Time Reversal for wireless communications


1
Time Reversal for wireless communications
  • Persefoni Kyritsi
  • PhD class on Adaptive Antennas
  • Aalborg, Dec04

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
Effect of delayed copies
8
Wideband systems
  • How do we define wideband systems?
  • Delay spread gtgt Symbol Time
  • Definition of the delay spread
  • Whats 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 gt 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
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
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
rd
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
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
FWA Measument locations
25
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 envts
  • MIMO channel models (Erceg et al 03)
  • Correlation-based model
  • Clustering in
  • Time (Saleh-Valenzuela)
  • Angle (AoA and AoD)

30
From SISO to MIMO
SISO channel
MIMO channel
31
802.11n MIMO channel models
  • DS between 15ns and 150ns
  • (BW802.1120MHz, BWmodel100MHz)
  • 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 (NTX2)
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
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
Multi-user operation
  • Nu2
  • Antennas of 2 different terminals at locations
    along the route separated by distance d

Measurement route
41
Measurements
  • fc2.14GHz,
  • BW gt 7MHz
  • 2 measurement routes of l1km
  • Transmitter
  • 8 TX antennas
  • htx20m (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 NR2, multi-user
With power control
Without power control
43
Applications of time reversal
  • Cable replacement
  • Military
  • Sensor networks
  • Other ???

44
What if we dont 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.
  • CSIMOCMISO(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.

51
LPI in HDB channels
  • How to interpret Low Probability of Intercept
    (LPI)
  • Security
  • Q how do we make security work today?
  • TR can achieve LPI even with 1 transmit antenna.
  • The power delivered per transmission can be very
    low, but the power from several transmissions
    will add up.
  • Are there commercial applications for LPI?

52
Opportunities for generalized TR
  • Pure TR can provide spatial focusing (spatial
    matched filtering).
  • Pure TR cannot completely eliminate ISI.
  • Look into schemes that
  • Remove ISI.
  • Keep spatial focusing.
  • A few ideas
  • TR in distributed antenna systems.
  • Sub-array selection.
  • Filter design.
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