Simulation of MIMO Antenna Systems in Simulink and Embedded

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M. Viberg, Chalmers University of Technology, T. Boman, FOI, U. Carlberg, SP,
L. Pettersson, FOI, S. Ali, Barcelona University (Chalmers M.Sc.), E. Arabi,
University of medical science and technology, Khartoum Sudan (Chalmers M.Sc.),
M. Bilal, Barcelona University (Chalmers M.Sc.), O. Moussa, KTH (Chalmers M.Sc.)
Sponsored by SSF within the Strategic Center Charmant
© 2008 The MathWorks, Inc.
Simulation of MIMO Antenna Systems
in Simulink and Embedded Matlab
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MIMO systems have multiple antennas
at transmit and receive
Basestation
Wireless LAN
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Why do we need MIMO systems?
The capacity for a SISO channel was given by Shannon
as
C = log 2 [1 + SNR ]
[bit/(Hz·s)]
Future wireless (data) applications will require increasing
data rates and therefore higher capacity
Spectrum (bandwidth) is very expensive
Increasing power (SNR) does not help much
Using multiple antennas more promising way to increase
capacity – ultimately linear in number of antennas!
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What can be achieved by multi-antenna
systems?
Array gain: Coherent combining will increase SNR
(Signal-to-Noise Ratio)
Interference rejection: Spatial filtering will improve SINR
(Signal-to-Interference-plus-Noise Ratio)
Diversity: Receiving more than one copy mitigates fading
Spatial multiplexing: Increase in data rate
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MIMO wireless communication system
Transmitter with
n antennas
Channel,
mxn
Receiver with
m antennas
RF part, focus of
this presentation
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Simulation of a MIMO system: flowchart
Main difficulty: how to
represent and compute
the overall ”transfer
function” (MIMO and
possibly non-linear)
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Overview of Simulink simulation model
Antennas, etc.
Power amp
Channel
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MIMO communication channel model
Multipath model, signal received via K paths:
Time-delay
of path k
Received signal
at antenna i
Response from i to j
via path k
Doppler shift
Transmitted signal
from antenna j
• Transmission paths generated according to ”Random Cluster Model”
• Can also use ”deterministic” parameters according to known scenario,
or a measured channel
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Simulink implementation
MIMO channel implemented as an FIR filter in Embedded Matlab,
and included in the Simulink model:
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Simulation of front-end electronics
Antennas, etc.
Power amp
Channel
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Amplifier non-linearities: ”Memory
polynomial” model
Easy to fit
parameters to
measured data!
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Simulation of antennas and network
Antennas, etc.
Power amp
Channel
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Microwave components characterized
using scattering parameters
−
in
V
+
Vout
Antenna or other
component
Vin+
−
Vout
S11 : Reflection coefficient
S 21 :
Transfer function
S:
Scattering matrix
In general, these are
frequency-dependent
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Multiple antennas: mutual coupling
• Single antennas conveniently handled in Simulink’s RF Blockset
• Multiple antennas cannot be represented due to the mutual coupling;
need multiport representation – ”matrix of scattering matrices”
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Modeling MIMO Network between PA and
Antennas
0 0
S
 S
TimeDomain
PA
SL1
SR1
(2x2)
(2x2)
SM
(4x4)
PA
Power Amplifiers
mutual
 S 11 11 0 TimeS 22
 1S mutual0 Domain
 1  0
0 
0 Frequency1 SDomain


22 
0
1
0
0
0 
0

0
SR
(4x4)
SL2
SR2
(2x2)
(2x2)
Matching
Network


0
Antennas
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Implementation in Simulink
The overall transfer function from PAs to antennas (MIMO) is
computed in Embedded Matlab, then implemented in Simulink:
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Simulation example using 2 transmit and
3 receive antennas
Training signal used to estimate MIMO equalizer:
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Transmitted, received and restored
signals (I and Q channels)
Tx
Rx
Tx
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Bit errors rate increasing due to timevarying channel
Tx
Rx
Errors
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Conclusions
MIMO antenna system implemented in Simulink with
Embedded Matlab
Can be used to test the impact of various components
(antennas, PA non-linearities) on bit-error-rate etc.
Future work:
Include signal processing according to various standards
(WCDMA, WiMax, LTE, etc.)
Optimization of components (and algorithms) with ”system-level”
design criteria
Increase flexibility and user-friendliness
Make the code publicly available
Other applications (radar and other microwave sensing)
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