Smart Antennas in Cellular CDMA- Systems

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Smart Antennas in Cellular CDMA- Systems

• Adrian Boukalov
• adrian.boukalov_at_hut.fi
• Helsinki University of Technology
• Communications Laboratory

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Content
1. Introduction 2. Smart Antennas classification.
Basics of Smart Antennas (SA) techniques 3.
Smart Antennas in CDMA systems 4. Network control
and planning with Smart Antennas. System
performance. 5. Future evolution. -
Glossary - Bibliography
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"Spatial Processing remains as the most
promising, if not the last frontier, in the
evolution of multiple access systems"


Andrew Viterbi
There are very few techniques proposed today,
which are able to improve radio
network performance dramatically - Spatial
processing - Multi-user detection - Channel reuse
based on polarization - Advanced network
control Spatial processing is among them and can
be effectively combined with others techniques
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1. Smart Antenna TechnologyMotivation
Link level improvements System improvements
- Interference cancellation
on the up and down links
and/or spatial
multiplexing - SNR improvement due to antenna
gain - Multipath mitigation
capacity coverage Quality
of service, bit rate, mobility rate
5
1. Smart Antenna Technology Benfactors
Network capacity, coverage, filling
dead spots, fewer BSs, higher QoS, new
services...-gt revenues
New market for more advanced BSs, flexible
radio network control...
Higher QoS, more reliable, secure
communication, new services, longer battery
life...
Operator
OEM
User
6
1. Possible combinations of spatial processing
with other techniques
Time domain processing (Equalization, RAKE,
) Diversity (polarization, additional
macro,..) Coding (ST coding) MU detection Link
adaptation
Spatial processing

7
1. Smart Antennas can be used at
- A. BS only up-link..coverage (HSR)
down-link..coverage capacity, spectrum
efficiency due to
reuse between cells (SFIR), due
to reuse
inside cell (SDMA), both SDMASFIR
- B. MS/subscriber only up-linkdown link
capacity due to tighter channel reuse
down-link....coverage capacity (WLL
applications) - C. Both ends MS and BS
simultaneously..coverage capacity (AB)
higher bit rate up-link due
to spatially multiplexed parallel channels and
down-link split high bit rate data
streams between them
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1. IntelliWave Wireless Local Loop System
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2. Smart Antennas in Mobile Communications on
the Globe
ArrayComm (USA) - installations in WLL - tests
for GSM 1800
Radio Design AB (SW) NMT-450
Ericsson (SW) first system system solution
with SA GSM
GigabitWireless(USA) WLL
NTT DoCoMo (Japan) Testbed for UTRA
IntelliWave Wireless Local Loop System
Raytheon
(USA)
Commercially available
Fully
Adaptive Smart Antenna System
TSUNAMI-SUNBEAM Project (EU)
- Wide range of RD activity
ARPA (USA )/GloMo project
- Recommendations for standardization
Metawave
(USA)
- Field Trials GSM/DCS 1800 system
Commercially available
Coordinator
ERA Technology
(UK)
Participants
IntelliCell
Motorola European Cellular Infrastructure
Division UK
Switched Beam System
France Telecom CNET France
University of Aalborg Denmark
Bosch Telecom GmbH Germany
Orange Personal Communication Systems Ltd. UK
DETyCOM Spain
University of Bristol UK
Polytechnic University of Catalonia Spain
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2. Smart Antenna Receivers Many choices!
- Switched beam, adaptive algorithms.. - Side
reference information available (spatial
reference, reference signal, signal structure
and their combinations) for spatial processing
- Narrowband , broadband (CDMA) - Optimization
method (if any) maximum likelihood-ML, minimum
mean squared error- MSE, minimum variance-MV, ...
- Domains -gt Space-only, space-time,
space-frequency - Amount and type of channel
knowledge available - Combination of
space/space-time processing with other
technologies (diversity, interference
cancellation, channel coding, space-time coding
) - Up-link, down-link. Smart antennas at the
mobile
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2. Spatial Processing Approaches
- Sectorization - Macro-diversity with
Combining maximum ratio combining - MRC
optimum combining -OC,.. Prefiltering/Coding
Space -Time Coding V-BLAST - Beamforming
(BF) Switched-beam Smart Antenna Adaptive
beamforming These approaches can be/should be
combined/mixed
together !
Sectorization
Macro-diversity
Switched-beam ant.
Adaptive BF
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2. Beamforming Methods
Data independent beamforming (conventional beamf
ormer -CBF,..) Optimum BF - Based on the
cost function maximization/minimization (max
SINR,) - Based on statistical estimation ML
(likelihood function) Squared function based
MSE (Reference ) -Adaptive algorithms - Least
Square (LS), Maximum A-posteriori Probability
(MAP),
( for example, GSLC,)
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2. Optimization Criteria
- Based on cost function maximization/minimization
(max. SINR,)-gt difficult to obtain - Based
on Statistical Estimation ML (Likelihood
function)-gt treats interference as temporally and
spatially white Gaussian. Balance effect of
noise. MSE (Reference )-gt more attractive in
presence of correlated CCI. -gt More efficient in
interference dominant environment. Do not balance
effect of noise
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2. Possible SA receivers realizations
Parameters that can be optimized
Data, BER
SINR
Time Ref. post det.
CIR
Time Ref.
Demod.
Detection
RF
IF
RF-BF
IF- BF
BB- BF/OC
Can be combined
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2. BF/OC Techniques Classified by Reference Type
Data-independent beamforming
- Spatial reference based beamforming,
Direction of arrival based beamforming
(DoABF) - Reference signal based/time reference
beamforming (TRB) and/or optimum combining (OC)
- Signal structure (temporal /spectral) based
beamforming, SSBF/property restored beamforming
Statistically optimum beamforming
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2. Direction of Arrival Based Beamformers
(DoABF )
- require angle of arrival (AoA) estimation -
sensitive to AoA estimation errors, calibration
problem - estimates output power at the output
or eigen-decomposition of correlation matrix -
problem with coherent multi-path - angular
spread to array resolution ratio should be
low - FDD applications
Array Processor
Array Output
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2. AoA estimation methods
1. Conventional techniques -
poor angular resolution limited by aperture,
search of peaks in spatial spectrum -
MV (some degrees of freedom spent on interference
cancellation, improved resolution) 2.
Based on statistical model of signal and noise
(optimal) - ML, MLM - data samples lt-gt AoA
joint pdf of sampled data needed, very
computationally extensive, can work well in
low SNR (or number of signal samples is small)
work well in correlated signal conditions,
number of sources should be known, non-linear
multi-dimensional optimisation (coincides with
LS estimator if assumptions about noise do not
hold)
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2. AoA estimation methods (contd)

3. Based on the model of the received signal
vector - high resolution methods , fail in
coherent multipath (suboptimal, BB only )
- MUSIC, WSF - ESPRIT subarraying (relaxed
computational and calibration
requirements) Supplementary techniques
required N sources, R- correlation matrix
estimation DOA estimation under coherent
conditions Spatial smoothing,
multi-dimensional MUSIC, ILSP-CMA,
integrated approach to AoA estimation.
U(t)As(t)n(t)
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2.Time-Reference Signal Based Beamformers and/or
Optimal Combiner (TRB/OC)
- requires reference signal or replica
correlated with desired signal which is
multiplexed with desired signal or
reconstructed from detected symbols - better for
varying radio channel - synchronization
problem - more processing extensive methods -
diversity - TDD applications - receiver is
simpler at expense of spectral efficiency -
delay spread to frame length ratio should be
low
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2. Signal Structure Based Beamforming (SSBF)
- does not require reference signal, thus
increased spectral efficiency - constant
modulus (CM) property of phase modulated
signals, - finite alphabet (FA) property of
digitally modulated signals , - spectral
coherence restoral SCORE (only information
needed - bit rate) - useful method for
tracking between references - convergence
properties ? - performance from robustness
point of view similar to reference signal
based methods
BF (W)
CMA
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2. Adaptive Algorithms
Tracking in time
Data independent BF
AoA estimation..
AoA(s)tracking ML, ...
- DoABF - TRB and/or OC - SSBF
Calibration
Ref. multiplexed with des. signal or
reconstr. from detected symbol
Adaptive Alg. DMI, LS (LMS, RLS),non-linear
Synchronization
Constant Modulus (CMA), FA,...
CM-LMS
Statistically Optimum BF
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2. Achievable improvements with spatial
processing
- Improvement in SNR. (improved coverage. ) -
Reduced ISI. (depends on angular spread of
multipath) - Enhanced spatial diversity. -
Interference cancellation. In Trx and Rx.
Capacity. These goals may be conflicting. Need
balancing to achieve synergy with propagation
environment, offered traffic, infrastructure.
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2. SNR maximization
Combining. MRC
Beamforming
Co-phased signals weighted proportionally to
noise level/antenna
1/M
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2. Co-Channel Interference (CCI) Cancellation
Beamforming
Combining
M-1 interferers cancellation. independent of the
environment
M-1
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2. Diversity (Angle- and Space-) Gain
M
M
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2.ISI Cancellation
M-1 delayed signals cancellation or (M-1)/2
symbol due to delay spread
M-1
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2. Optimal Spatial Algorithms
Beamforming
Multi-path
BS
Interfering MS 2
MS 1
Path with ISI, uncorrelated paths
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2. Optimal S-T Algorithms
Equalization

Spatial domain processing Temporal
domain processing
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2. Degrees of freedom number of SA
elements
- Number of SA elements (M) can be
considered as a resource, i.e. degrees of
freedom which can be spent for SNR, CCI,
diversity, ISI, either separately or jointly
(optimum) - M determines spatial
selectivity of SA
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2. Spatial Processing Summary
DoABF - better perform in environments with low
angular spread - require AoA estimation and
calibration - well suit for FDD applications -
macrocell environment - in CDMA AoA estimation
and beamforming can be different TRB or/and
OC - well perform in environments with high
angular spread - require reference signal
(spectrum efficiency), synchronization - well
suit for TDD (micro/pico cells), FDD is more
problematic - micro and picocell - more robust
methods in changing environment (adaptive
algorithms)can be/should be combined with blind
methods
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2. Space-Time (S-T) Processing Techniques
Decoupled S-T processing Joint S-T
processing Path diversity BF Combining Single
user MU Narrowband Wideband Up-link Down-link
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2. Space-Time (S-T) Processing
- Space domain processing Efficient CCI
mitigation Space Diversity ISI mitigation
depends on angular spread of multipath and M
and cannot be very efficient - Time domain
processing Very limited against CCI Time/path
div., ISI mitigation - S-T Processing
Simultaneous operations in Time and Space
domains can combine strength of the both -
Multi-User-S-T Processing
Channel
ST-MLSE
Vector VA
Sk

Training
ST-MMSE
yk
Sk
Demod.
W

ST-MMSE/MLSE
STF W
Scalar VA MLSE
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2. Space-Time Channel Estimation
Underlying channel/signals structures
Tracking of varying channel
Channel Estimation methods
Reference
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2. Macrocell and Microcell Channel Response

Macrocell
Microcell
Remote scatters
1800
1800
Scatters local to BS
-1800
0
1
0
20
Delay (microsec)
Delay (microsec)
Scatters local to MS
After A.Paulraj
- Smart Antennas algorithms should be optimized
according to the propagation environment based on
the cell by cell principle
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2. Summary
- Spatial structure based algorithms can work in
higher Doppler spread but are affected by
angular spread - Temporal structure based
algorithms can better handle delay spread, but
higher speed can be problem - Single and
multi-user combination may be needed - Training
signal lt---gt receiver complexity trade-off -
Environment (spreading) lt--gt receiver and
algorithmic complexity, (how models
corresponds to reality)
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2. Summary (contd)
Best solutions Combine trade-offs between -
Beamforming lt---gt combining - Algorithms
(MLlt---gt MSE) , subspace - Optimum lt---gt Data
independent approaches - Baseband beamforming
lt---gt RF/or IF beamforming - Combination with
other methods like multi-user detection (MUD),
diversity, ST coding, adaptive modems Air
interfaces should be not only friendly for
S-T processing but flexible / adaptive to be able
to exploit advantages of spatial processing in
variable environments
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- In non-multiuser case users are seen as
interference to each other and there are many
weaker CCI in the uplink. - Multipath gives
rise to the MAI due to the losses of codes
orthogonality. - Code can be seen as a free
reference signal - ISI compensation has less
importance in CDMA than interchip interference
(ICI). But for very high bit rate ISI
cancellation may be required. - Wideband
beamforming realization and methods of AoA
estimation are different from narrowband -
Channel estimations can be based on spreading
codes and it presumes introduction of novel
techniques
3. CDMA SA Receivers
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3. Smart Antenna CDMA Receivers
- In- coherent combining (equal gain diversity
combining improves SNR, but CCI cancellation
not possible.) - Coherent combining
Beamforming- RAKE (1D, 2D) Reference signal
based beamformer - RAKE DoABF - RAKE (max.
SINR, ML, ..) SSBF- RAKE Combing - RAKE
OC, IRC,.. - Joint S-T processing based on
channel estimation (MMSE,...) - Multi-user ST
(MU-ST-MMSE, MU-ST-MLSE) - Space -frequency RAKE
(RS-F) joint, and decoupled
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3.Classification of Smart Antennas for CDMA
Multi-user ST. MMSE, MLSE
H, H, H,...
Single-user joint S-T MMSE, ..,..
Diversity combiner
H1
Spatial filter w single/ multi user
RAKE receiver
S-T Combiner w RAKE receiver
Ant.
RS-T
or
Ref. - AoA, Code
H1
RS
H1
Ref. Pilot, AoA
Chip level BF, combiner
Symbol level BF, combiner
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3. CDMA Rx Structures (Ch. Knowledge lt-gt
Optimality)
S-DIV T-DIV MUI X X X
X X X X
X X X X
X
ST-MU
H1 H2
H1 RS-T
ST-MMSE
ST-RAKE
H1 RS
Decreasing Channel Knowledge
BF-RAKE
H1
ANT-HOP
Nil
After A. Paulraj
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3. Performance of CDMA SA Receivers - For
low SNR sophisticated spatial-based blind methods
are not efficient (switched-beam) - User
dedicated pilots at the up- and down-links -
additional advantage for SA technology especially
in highly loaded cells.- In CDMA the forward
link channel estimation problem is simpler than
in TDMA because it is possible to decouple the
channel mapping for each path and deal with lower
angle spread.- In CDMA SA receiver is less
sensitive to channel estimation errors but beam
pattern optimization can be is more complex. -
In multi-bit rate CDMA SA receiver can
successfully cancel interference coming from the
limited number of high bit rate users thus
considerably increase system capacity .
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3. Wideband Beamforming (TDL filter)
T
T
T
Output
T
T
T
- wideband BF combines spatial filtering and
temporal - TDL can flattenthe spatial
response as function of frequency
(equalization) - it can be used as an adaptive
interference rejection filter - 2D RAKE can
provide some of the same benefits of WBF with
less complexity
Optimum filters with specify rejection response
Weighted Chebyshev method
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3. CDMA SA Receiver
- Path diversity can be achieved with WB Array
and RAKE which is WBA with only few taps,
and variable matched delays of the received
multipath components which followed by diversity
comb. - Single-user and multi-user SA
receiver demodulate simultaneously K signals.
Estimation- subtraction (Spat Proc. Par. IC).
Separable MAI cancellation in space domain. IC
for remaining MAI - 2D RAKE achieve angular and
temporal separation - dispersing -gt spatial
receiver requires only one despreader for each
spatial receiver - reverse spat filter -gt
despreading - M (branches) despreaders are
required
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3. Spatial Processor and Parallel Interference
Canceller
Antenna
User 0 MF
w
For user 0

Delay
w


.
-
User 1 MF
Regenerate user 1
v1
w
.
User K-1 MF
Regenerate user K-1
Vk-1
Weight Update
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3. Wideband SA Receivers. BF S-Time RAKE
(single user approach)
Beamformer switched-beam
AoA BF ( multi-targ. BF) Eigenfilter
Method Ref. Signal CMA
Space-Time Matched Filter
Balanced QPSK BF-RAKE receiver with
coherent combining
Balanced DQPSK BF-RAKE receiver with
incoherent combining
SNR
46
3. Signal structure (code) based beamformer for
IS-95.
Alorithm - Perform code filtering for each user
and for each element - Estimate array pre- and
post- correlation matrices Rxx and Ryy,1 -
Estimate the ch. vector a1 corresponding to the
largest generalised eigenvalue of the matrix pair
(Rxx, Ryy,1 ) - estmate the interference plus
noise covariance Ruu,1G/G-1(Rxx-(2/G)Ryy,1)
(G-proc. gain) - find optimum weight vector w1 R
uu,1-1 a1
After A.F.Naguib
- code-filtering exploit spatial and temporal
signal structure Eigen.
47
3. V-RAKE and MDIR receivers(SUNBEAM Project)
48
3. Interference rejection combining (IRC).
IRC combiner
w
A/D
A/D

A/D
LMS/RLS
Pilot
Delay est. for path L

RAKE
w
Finger L1

Finger LK
Pilot
LMS/RLS
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3. NTT DoCoMo SA testbed
MMSE BF
MF
A/D
RAKE/ch.est.
MF
A/D
Tentative data decision
MF
A/D
ith- finger processing
- BF based on DD MMSE using data symbol and pilot
50
3. Experimental results. Performance. (NTT DoCoMo
testbed for UTRA)
Performance Comparison SA and Space Diversity
Average BER Performance
10-1 10-2 10-3 10-4 10-5
10-1 10-2 10-3 10-4 10-5
Average BER
Average BER
Space diversity
Adaptive array
0 5 10 15 20
-5 -10 -15 -20 -25
Average Eb/N015dB
SIR (dB)
51
3. S-T MU-MMSE
52
3. ST MU-MLSE.
Multi-user VA channel estimator for input
sequence for each user
Bank of S-T matched filters - whitened MF
correlator-gtTDL w(t,?)
Antennas
Steering Matrix Q
Channel filter h(t)
received signal
received sequence for each user
Transmitted sequence for each user
- computational complexity linear to the number
of users - same degree of near-far resistance and
error rate performance as optimum MU receiver -
require knowledge of all users channels -
optimum in Gaussian noise only
53
4. Feasibility of SA with Cellular Network
Network Planning - Capacity, coverage,
interference planning - Joint fixed and radio
network optimization, planning -
System upgrade, economical issues
1G- analog systems 2G- digital systems 2.5G-
digitalpacket .. (GPRS,..) 3G - W-CDMA 4G-
cellular gigabit WLAN
Radio Interface Receiver structure, Tx, Rx
algorithms - Spatial proc. - Time domain proc. -
Coding - Detection - Diversity - ..
Radio Network Management
DSP tech. SW Radio
Services -gt MS location
Network control - R.resource management - call
control
Cell control - admission control - broadcast
channel control - handover control -
macro-diversity control
1G
2G
Air Interface - Multiple access - Duplexing -
Modulation - Framing - Availability of pilots
Link level control - Power Control - Quality
Control - Tracking
2.5G
3G
4G
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4. Smart Antennas integration with air interface

Antennas elements geometry, numbers of elements -
M.
Radio Transmission Technologies
MS
Internetworking
Physical Channel Definition, Multi- plexing
Multiple Access Technology
Frame Structure
Duplexing Technology
RF- Channel parameters
Channel Coding
Modulation Technology
Source Coding
Availability of the training signal Frame length
- T
Mapping control, traffic channels
FDD TDD
Modulation type CM... Finite Alphabet Linearity
FDMA CDMA
Combination with Space Processing
Bandwidth-B Carrier frequency fo
UL-gtDL link
Wide/narrow band SA rec, BF, AoA est
Blind methods SSBF, ST

Ref. Signal based BF, S-T
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4. Notes on feasibility with different air
interface standards
Analog systems only primitive SA receiver can
be used Digital GSM - / dedicated sounding
sequences may be needed network control
(protocols) inter cell synchronisation IS
-95 - auxiliary pilot is needed UTRA
user dedicated pilot
56
4. Network Planning with SA
- Concepts of HSR, SFIR, SDMA.. in F/TDMA
networks - CDMA network planning with SA -
Networks upgrade with SA - Simulation tools
57
4. Three Stages of Introduction Adaptive Antenna
Technology in Cell Planning Process
1. High Sensitivity Reception (HSR) 2. Spatial
Filtering for Interference Reduction (SFIR) 3.
Space Division Multiple Access (SDMA) 4.
SFIRSDMA ?
58
4. HSR concept
- SA at the up-link only - Gain approximately
10logM - with 8 elements reduction of number
of BS by factor of 0.3 only by factor of 0.5
with diversity - revolving beam technique
improve coverage of BCH
59
4. SFIR concept
- CCI cancellation SA at the down-link -
capacity improvement of 2.5 require 6dB CIR
improvement (already achieved by Ericsson with
simple SA algorithms) - the same range extension
as with HSR - simulations shows that
approximately the same capacity gain can be
achieved with SFIR and SDMA while SFIR require
considerable less network control upgrade
60
4. SFIR concept
- it was found reasonable to combine in GSM
SFIR with random slow frequency hopping to
benefit from interference and frequency
diversity - reuse factor 1/3 seems reasonable
1/1 possible but too complex since dynamic RR
management based on CCI measurements is
required - frequency re-planning, but network
control (RR) less affected
61
4. SDMA concept

- expected up to 8 times capacity improvement -
power classes concept (can be dynamic or
static) - with ref. signal BF MSs can be
separated even when they have the same angular
position to BS ! - for DoABF MSs angular
distribution is important (macrocell) -
network planning (frequency) is simpler, but
larger cell size can require new planning,
more smooth migration into existing network -
more network management upgrade required
PCH 1
PCH 1
PCH 1
PCH 1
62
4. SFIRSDMA concept
- In theory it is possible to combine SFIR and
SDMA concepts - Intercell reuse distance and
intracell reuse distance of co-channels will
increase - Complexity is very high to be
implemented in the near future
63
4.Improvements in system performance with SA
HSR
SFIR
Reduction of the number of BS sites with HSR
10.0 8.0 6.0 4.0 2.0 0.0
Spectrum efficiency gain of SFIR
3.0 2.5 2.0 1.5 1.0 0.5
0.9 0.8 0.6 0.4 0.2
BS reduction factor
Range extension factor
Efficiency gain
Range extension with HSR
- 25 load, optimized --- 50 load , optimized
0 5 10 15
20
0 5 10 15
20
Number of elements
Number of elements
64
4.Improvements in system performance with SA
SDMA
25 20 15 10 5 0.0
Spectrum efficiency gain of SDMA
Efficiency gain
- N M-1 - N M/2 .-..-.. N4
M -number of array elements N - number of
parallel beams
0 5 10 15
20
Number of elements
65
4. Network upgrade with SA
- smooth migration is possible and even
several BS with SA can provide considerable
capacity improvements - feasibility of
sectorization and SA (3-4 sectors with SA
based on ULA) - down-link considered as more
problematic by network planners in
interference limited network - introduction BS
with SA can increase amount of channels and
level of CCI in the neighboring cells. Balance
can be achieved.
Nch,CCI
BS

BS with SA
BS
BS
BS
Ptrx_tot
SA
66
4. CDMA network planning with SA
- reuse factor 1, only HSR and SFIR concept is
applicable - in multi- layer ( single
carrier ) CDMA network (?) SA can reduce
near-far effect - range will increase -
pilot pollution problem can be solved by control
it spatial domain - cells breathing effect
can be mitigated - capacity will increase since
less interference at the receiver - SA can be
very effective in suppression interference coming
from the limited number of high bit rate users
- PN planning
67
4. System performance with SA in CDMA
SNR10log (M) dB
Ga 4.8 dB
1200
BS
Cells geometry
Number users supported in each cell (Eb/N09 dB,
v0.6, n-path loss exponent)
Rate Set 1 Rate
set 2 (N19.3 dB)
(N21.1 dB) Antenna Ga n2 n4
n2 n4 Omni-dir. 0 12 18
8 12 Sectored 4.8 36 55
24 37
BS 0
68
4. Impact on network planning
69
4.NetSim simulation tool for study network
control and planning with SA
Two Users LOS propagation scenario
Center of Helsinki
-75 dB
300
120
60
MS1
-80 dB
160
30
250
-85 dB
250
180
0
200
BS
330
150
210
240
300
270
100
MS2
- incoming impulses from the MS1 - amplitude and
AOA
50
- incoming impulses from the MS2 - amplitude and
AOA, considered as interference for MS1 (and
vs)
0
250
300
150
0
50
100
200
basis X-coordinate
- Smart Antennas radiation pattern antenna main
lobe locked on the signals coming from MS1
70
4. Network Control with SA.
- power control. Quality monitoring. Tracking. -
initial access, handover (HO), initial access,
- resource management - broadcast channels
control with SA - resource management -
services layer . Geolocation.
71
4. Layer 1. Power control. Quality monitoring.
Tracking.
- power control at up and down links is
beneficial (60 more capacity ) (Downlink in
SDMA can be problematic due to furthest
mobile) - dynamic behavior of tracking power
control ? - user identification problem to
support SDMA individual color codes needed to
support each SDMA traffic channel channel, also
for admission control .. - for rescue
purposes omni directional channel for call
recovery is proposed - power classes concept
(SDMA, others ..? )lt --gtRR management(
tradeoff needed to avoid trunking effects)
72
4. Layer 2. Initial access. Handover.
- location aware HO or through
omni-directional channel ? - initial access with
omni directional channelgt narrow beam or
transition wide beam gtnarrow beam - to setup
beamformer just before user dedicated channel
is allocated (access procedure modification or
increased access time ) - delayed handover
while new BS has not been localized - how to
make down-link BF when channel info. at the
up-link is not available yet (temporal
omnidirectional downlink or longer access)? -
to allow different synchronization sequences -
packet capturing by SA can improve packet
transmission via random access channel
Initial access
t
73
4. Broadcast channels control with SA
- revolving beam concept in TDMA (more feasible
for coverage extension) neighboring cell
monitoring can be more problematic . Frame
structure... - control cell coverage by
reshaping transmitted antenna pattern
(sectorized and non-sectorized) - auxiliary
pilot is needed in IS-95 (since omni-dir. pilot
and beamformed traffic channels propagate
differently), can be assigned for cluster of
MS or single MS - should be considered at
network planning - need to split carefully
beamformed and omni-directional channels ..
slot 1
slot 2
BS
slot 3
Pilot/BCH
sector2
BS
sector1
74
4. Layer 3. Resource management.
- new functions physical channel allocation
based on angular

information and or link quality monitoring -
dynamic channel allocation (DCA)
(localization with different precision... ??
needed) gt precise localization - centralized
DCA or gt no DCA with SFIR and interference
averaging approach or gt subdivision on sectors
and create list of forbidden sectors - joint
power control , beamforming and BS assignment -
centralized or distributed control (bunch
concept) ? - smoothing of spatial traffic
distribution - more benefit we expect to get
(capacity,flexibility)- more RR
management should be aware of spatial
characteristics
75
4. Network Control with SA. Higher layers.
Geolocation.
New service (991, transport control)
Combined DOA measurements and time delay based
approach Raytheon introduced commercial
available geolocation system (SA option is
included)
76
4. Impact on the network control
Geolocation
based on
AoA
estimation
Service
layer
RR management
interference averaging
U
C
Layer 3
DCA...,
combined with user specific info

(color codes,
AoAs
)
Layer 2
Initial access , HO control
Reference signal availability
Layer 1
Multiple Access ,
Duplexing
,
PN, DTX.

Broadcast channels control
77
4.Network issues. Summary
- More benefits with SA- gt more Resource
management should be aware of - gt User
location (AoA,..)and/or - gt Power (power
classes ,...)and/or - gt Channel quality (and
spatial properties ?) Co-ordination
between BSs -gt at least loose form of
synchronization for time reference BF
(Layer 1) -gt exchange information about user
location and /or - gt channel quality (and
spatial properties ?) -gt exchange information
about cells traffic load
78
4. Network issues. Summary (contd)
- It is need to incorporate more user
dedicated information into channels (user
dedicated pilots, color codes, different
synchronization sequences) to separate/identify
users (implemented in new air interfaces cdma-
2000,UTRA) - Channels structure should be more
carefully divided between beamformed and
omnidierctional. Minimize blanket coverage in
terms of frequency/time - DTX(comfort
level?), HO, initial protocol perhaps should be
slightly modified, but it can increase signaling
overheadgt more interference in CDMA -
combination with link adaptation (since at the
beginning channel history is not available).
This combination will increase soft capacity
limit - some changes can be expected at the MS
(receiver, ant., protocols)
79
5. With Smart Antennas where we go ?
1 -gt Integration into existing
systems (.) 1A -gt System modification (perhaps
locally at few BSs) to achieve better performance
with SA Equipment , frequency reuse , new
services (???) 2A -gt SA integration into
various WLL applications (.) 2 -gt SA
integration into standartized
WLL (..) Perhaps in future we should
combine 1 2A (or 1A ?)
Modified Systems ???
1A
AMPS,NMT GSM,IS-95
reuse
1
Smart Antennas Tech.
Layer 1
2A
WLL standardized
WLL
2
80
Glossary
81
Bibliography
- J. C. Liberti , T.S. Rappaport Smart Antennas
for Wireless Communications, Prentice Hall
PTR, 1999. - F. Swarts, Pieter van Rooyen, I.
Opperman , M.Lotter CDMA Techniques for Third
Generation Mobile Systems, Kluwer Academic
Publishes, 1999. - L.C. Godara Antenna Arrays
and Mobile Communications,Part I, II, Proc. of
IEEE vol. 85, NO. 8, July , August 1997. - H.
Krim, M. Viberg Two Decades of Array Signal
Processing Research, IEEE Signal Processing
Magazine , July 1996. - A. J. Paulraj, B. C. Ng
Space-Time Modems for Wireless Communications,
IEEE Personal Communications, February 1998. -
B. D. Van Veen, K. M. Buckley Beamforming A
Versatile approach to Spatial Filtering, IEEE
ASSP Magasine, April 1998. - P.M. Grant, J. S.
Thompson, B. Mulgrew Adaptive Arrays for
narrowband CDMA Base Stations,
ElectronicsCommunication Eng., Journal, August
1998 - J. H. Winters Smart Antennas for
Wireless Systems IEEE Personal
Communications, February 1998. - R. Kohno,
Spatial and Temporal Communication Theory Using
Adaptive Antenna ArrayIEEE Personal
Communications, February 1998.