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

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Effect of delayed copies
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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?

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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)

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Fundamentals of TR

• Applicable in channels with LARGE delays spread
  (ds x bw gt 20)

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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.

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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)
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Time reversal Frequency domain
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Why TR?

• Benefits
• Temporal focusing
• Spatial focusing
• Channel hardening

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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

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Spatial focusing with TR
r
rd
Interference (IF)
At the sampling time
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Demonstration of MISO TR
(au)
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Demonstration of SISO TR
(au)
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Demonstration of MISO TR
(au)
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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

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Exp 1 MISO TR to a single user
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Advanced weighting schemes

• TR with antenna weighting
• Weight selection algorithms

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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

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FWA Measument locations
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Classification of MISO situations
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Delay spread reduction ?heq/?h
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Explanation The shower curtain effect
Psycho (1960)
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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

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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)

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From SISO to MIMO
SISO channel
MIMO channel
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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)

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Notation

• Power on each tap

• Correlation properties of each tap

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Capacity
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Interference for SISO TR
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MISO spatial focusing (NTX2)
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Exp 3 Spatial focusing in MISO TR to multiple
users

• Each receiving antenna represents one of the Nr
  users

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Interference calculation

• Signal on target user
• Interference from other users
• The SIR

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Reminder The near-far problem
U1
U2
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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

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Multi-user operation

• Nu2
• Antennas of 2 different terminals at locations
  along the route separated by distance d

Measurement route
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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

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Results for NR2, multi-user
With power control
Without power control
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Applications of time reversal

• Cable replacement
• Military
• Sensor networks
• Other ???

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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?

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Desirable features

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

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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.

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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.

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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.

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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?

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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.

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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?

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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.