lecture10.ppt

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Postacademic Course on
Telecommunications
Module-3 Transmission Marc Moonen
Lecture-10 Smart Antennas K.U.Leuven/ESAT-SISTA
Module-3 : Transmission
Lecture-10 (18/5/00)
Marc Moonen
Dept. E.E./ESAT, K.U.Leuven
marc.moonen@esat.kuleuven.ac.be
www.esat.kuleuven.ac.be/sista/~moonen/

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Lecture-10 Smart Antennas K.U.Leuven-ESAT/SISTA
Lecture 10 : Smart Antennas -Overview
• Introduction:
Smart Antennas
SDMA (`driver application’)
• SDMA v1.0
Line-of-sight propagation & beamforming
DOA estimation and signal reconstruction
• SDMA v2.0
Multi-path propagation
MIMO channel modeling & source separation
• Related Topics
CDMA multi-user detection (see Lecture-9)
MIMO transmission (see Lecture-2)

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Introduction : Smart Antennas
• Antenna arrays (hardware) with (software) `beam-forming’
(`beam-steering’), or similar (in multi-path scenario, see
below).
• `Antenna diversity’

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Introduction : Smart Antennas
• Aim : increase signal-to-interference-and-noise ratio, hence
improved performance/increased capacity (e.g. in CDMA
systems)
• Antenna arrays mostly considered for base station
systems, not (often) for mobile terminals.
• Currently simple systems with switching between antenna
signals (=select best signal), fixed directional antennas for
sectorization (e.g. GSM), ...
• More advance systems considered for WLANs, for
W-CDMA, etc...
• Will consider SDMA as `driver application’

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Introduction : SDMA
• `Conventional’ wireless communications
(`SISO’, TDMA/FDMA/CDMA)
• What we have in mind is ….
(MIMO transmission, SDMA)
transmitter
radio channel
x bits/sec/Hz/km2 receiver
transmitter receiver
transmitter
radio channel
2x bits/sec/Hz/km2
receiver

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Introduction : SDMA
• Example : cellular mobile telephony (e.g. GSM)
• Basic network architecture :
-country covered by a grid of cells
-each cell has a base station
-base station connected to land telephone network and
communicates with mobiles via a radio interface

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Introduction : SDMA
• Why cellular ?
Capacity increase by spectrum reuse
, pico-cells, etc.
• Capacity increase by multiplexing :
- GSM (900MHz) has 125 frequency channels/cell (FDMA)
8 time slots/channel (TDMA)
In practice, capacity per cell << 8*125 !
- Spatial multiplexing : allows different users in 1 cell to
use the same freq./time slot
- Tool = DSP algorithms for signal separation, equalization
4
1
d
P 

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Introduction : SDMA
• PS: in GSM neighboring cells cannot use same
frequency bands (intercell interference). Same
frequency band used in each 7th cell.

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Introduction : SDMA
• SDMA (`spatial division multiple access’) allows different
users in the same cell to use the same frequency
channel/time slot/code, and thereby offers substantial
capacity increases when superimposed on a current
system!
• SDMA supports multiple directional connections on a
single conventional radio channel through the usage of
antenna arrays and advanced signal processing.

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Introduction : SDMA
PS: SDMA ~ `dynamic sectorization’
WARNING:
• Major practical impediment is computational complexity
(cfr. linear algebra-type operations at high sampling rates).
…Gflops requirement….
• Major challenge for VLSI/ASIC design
• First products probably in WLAN-type applications instead
of cellular/mobile
AIM:
• Illustrate (near) future system design concepts….

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SDMA v1.0: Beamforming Approach
• Assumptions:
- sources are in the far-field
- line-of-sight (LOS) connections
- no multi-path effects
- homogeneous medium/ideal channel characteristics
- additive white Gaussian noise (AWGN)
- no inter-symbol interference

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• Beamforming (`spatial filtering’):
PS: compare with regular temporal (FIR) filtering

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• Data Model:
= *
antenna outputs sources
array gain
matrix
time samples for antenna-1
time samples for antenna-2
time samples for source-1
time samples for source-2
steering vector source-1
steering vector source-2

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• Data Model:
`Steering vector’ a(theta)
= array response vector, contains gains and phase shifts
for a narrow-band wavefront impinging from direction-of-
arrival (DOA) theta (and for a certain carrier frequency)
The collection of `steering vectors’ for all possible angles
theta, is referred to as the `array manifold’
Knowledge of `array manifold’ is crucial is beamforming
approach

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• Array manifold example:
Uniform Linear Array
where f = phase shift =
d
angle
)
)
sin(
.
.
.
2
.
exp(
c
d
f
j c 
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2
...
1
)
( 
 M
f
f
f
a 


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• Significance of array manifold:
-array manifold is a parametrization of the steering
vector as a function of the DOA
-if array manifold is known (by calibration or
physical modeling), `channel modeling’ is reduced
to DOA estimation. If the DOA for one particular
source is identified, its complete steering vector is
known.

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• Problem Statement:
Given antenna outputs & array manifold,
compute :
-directions-of-arrival (DOA’s)
-source signals
= *
array gain
matrix
?

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• Solution (Part-1): DOA estimation
`low-resolution algorithms’ : Fourier-based (e.g. for ULA’s)
`high-resolution algorithms’ :
-MUSIC [Schmidt 1979]: search for DOA such that steering
vector optimally matches `column space’ of antenna output
matrix
-ESPRIT [Roy et al, 1987]: DOA’s identified as generalized
eigenvalues of a matrix `pencil’
= *
array gain matrix

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• Solution (Part-2):
Beamforming and signal reconstruction
Given steering vectors
of signal-of-interest and
interferers, compute
beamformer weights
such that interference
signals are eliminated
(`null steering’)

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Beamforming and signal reconstruction
compute weight vector w1, w2,…. such that….
= *
array gain
matrix
* *
w1
w2
:
w1
w2
:
1
0
:
0

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- Compute weight vector w1, w2,…. that cancels all
interferers, and retains the signal of interest (cfr. supra)
- This is `zero-forcing’ solution. With additive noise, a
minimum-mean-squared-error solution is preferred.
- Other : Generalized sidelobe canceller, minimum variance
distortionless response beamforming, Griffiths-Jim
beamforming : adaptive beamformers, based on
knowledge of steering vector of (only) the signal-of-
interest, and where noise environment (incl. interferers)
may be time-varying.

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• Beamforming approach deficiencies :
- not always line-of-sight (LOS) connection
- multi-path effects
long/short term fading (e.g. wavelength=30cm @ 900MHz)
- inter-symbol-interference
(e.g. symbol ~ 1km @ 270kbits/sec)
• Conclusion:
- array manifold concept no longer useful
- need more sophisticated data models/algorithms

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SDMA v2.0: Channel Modeling Approach
• Instead of this…..
• we have this…..
• now what??

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• SDMA with multi-path corresponds to multi-user (multiple-
input/multiple output) channel equalization problem :
a) identify channel model
b) reconstruct channel inputs from outputs+model
single-user (e.g. GSM) multi-user (SDMA)

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• Step-1 is a channel modeling, i.e. identify...
• Training sequence based versus `blind algorithms’
(see Lecture 5-6)
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• Step-2 is equalizer design, i.e. identify…
• Zero-forcing (ISI=MUI=0) versus MMSE (see Lecture 5-6)
• This is combined equalization & source separation
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• Step-1 & -2 may be combined : direct (training sequence
based) equalizer design (see Lecture-5/6). Only training
sequence for user-of-interest needed (not for other users).
• Recursive vs batch processing (Lecture-5/6)
• `Oversampling’ (i.e. having more outputs (antennas) than
inputs (users)) is crucial for the existence of zero-forcing
solutions (for FIR channels).
• Connections with fractionally spaced equalization theory
and filter bank theory.
• Active area of research (blind algorithms based on 2nd
order statistics, finite alphabet properties, etc.).
• Commercial use: probably first WLAN, etc...

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Related Topics
CDMA multi-user detection algorithms (Lecture-9)
MUD algorithms are conceptually similar :
-Spreading viewed as a (transmit) filtering operation
and part of the `channel’.
-Nyquist-rate sampling at the receiver is symbol-
rate oversampling, which is equivalent to spatial
oversampling (multiple antennas).
-etc...

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Related Topics
MIMO Transmission (Lecture-2)
- Point-to-point transmission, where both transmitter
and sender have antenna array
- additional flexibility for sender (beamforming, …)
- with M antennas at both ends, allows for M-fold
channel capacity increase with the same transmit
power budget (!)
- example : V-BLAST

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Conclusions
• Smart Antennas
- Advantages : improved signal-to-interference-and-noise
ratio, increased capacity (CDMA).
- Considered for W-CDMA, ...
• SDMA v1.0
- Beamforming approach
- Conceptually simple, but not applicable in multi-path
environment
• SDMA v2.0
- Multi-path/MIMO channel modeling approach
- Powerful but complex
• Related Topics
- CDMA multi-user detection, MIMO transmission

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Assignment 5.1
`Brain Teaser’ :
• In Lecture-2, we have considered MIMO-transmission from
a channel capacity point of view. Look at the conclusions
again. One of the conclusions was that `one has to be
lucky with the channel characteristic’.
• Think of a similar channel capacity analysis for SDMA.
Does one again have to be `lucky with the channel’ ?
• What would be a most advantageous channel, in terms of
channel capacity ? What would be the obtained channel
capacity ? Is it `what we had in mind’ (cfr slide-5) ?

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Assignment 5.2
`Brain Teaser’:
• In Lecture 7-8 we have considered multi-tone
transmission, where a (high-rate) bit stream is split up into
(low-rate) parallel bit streams, which are then used to QAM
modulate different carriers.
• Now consider these low-rate streams as being different
users, accessing the same transmission channel. The
carrier modulation may be viewed/compared with a
spreading operation a la DS-CDMA.
• Based on this, compare DMT with CDMA and MIMO, both
from a capacity and a receiver structure point of view.
Look for similarities and differences.
• In a similar fashion, compare DMT with MIMO transmission

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