Efficient and Refined Modeling of Wireless Sensor Network Nodes Using SystemC-AMS

Efficient and Refined Modeling of Wireless Sensor Network

Nodes Using SystemC-AMS

M. Vasilevski

H. Aboushady, F. Pecheux, N. Beilleau

Laboratory LIP6 University Pierre and Marie Curie, Paris 6, France

June 2008

1.Issues

2.SystemC-AMS Languagea. Models of Computation

b. SDF Behavioral Description

c. SDF Multi-rates

3.RF and AMS Modelinga. AMS Models

b. RF Models

4.Wireless Sensor Network Node

5.Conclusion

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1. Issues : Mixed Systems Design

A/D Converter Microcontroller RF Transceiver

SystemC

VerilogVHDL

Matlab

Verilog-AVHDL-AMS

Spice-RF

Matlab

Verilog-AVHDL-AMS

Spice

1.Issues

2.SystemC-AMS Languagea. Models of Computation

b. SDF Behavioral Description

c. SDF Multi-rates

3.RF and AMS Modelinga. AMS Models

b. RF Models

4.Wireless Sensor Network Node

5.Conclusion

M. Vasilevski Laboratory LIP6, University Paris6 5

SystemC Simulation Kernel

DE, MoCs(CP,FSM,

etc…)

Synchronisation Layer

OtherModeling

Formalism

LNModeling

FormalismSDFModeling

Formalism OtherSolver

LNSolver

SystemCSystemC-AMS

SynchronousData Flow

LinearNetwork

2.a Models of Computation

Models of computation :

•Conservative Linear network

•Synchronous Data Flow

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2.b SDF Behavioral Description

A

B

Inp

ut

Ou

tpu

tBehaviour

C

SCA_SDF_MODULE(B)

SCA_SDF_IN<double>SCA_SDF_OUT<double>

void sig_proc( )

f(input)Output

Sb...Sbb

Sa...SaaH(S) m

m10

nn10

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2.c SDF Multi-Rates

A B C1 2 1 3 2 1

8 Hz 16 kHz 48 kHz 24 kHz

rate_sample_in

freq_sample_in

rate_sample_out

freq_sample_out

s5.62 Simulation sample time

Simulation rates

Tin Tout

Cluster

1.Issues

2.SystemC-AMS Languagea. Models of Computation

b. SDF Behavioral Description

c. SDF Multi-rates

3.RF and AMS Modelinga. AMS Models

b. RF Models

4.Wireless Sensor Network Node

5.Conclusion

M. Vasilevski Laboratory LIP6, University Paris6 9

3.a AMS models : Integrator

S

A

SCA_SDF_MODULE (integrator){ sca_sdf_in < double >in; sca_sdf_out < double >out;

double f; sca_vector < double >NUM,DEN,S; sca_ltf_nd ltf1;

void set_coeffs(double A){ DEN (0) = 0.0; DEN (1) = 1.0; NUM (0) = A; } void sig_proc(){ out.write(

ltf1(NUM, DEN, S, in.read())); } SCA_CTOR (integrator) {}};

In/Out ports

Other Attributes

Initialisation method

Signal processing method

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3.a AMS models : Decimator

1z1

Decimator

21z1 2 1z1 2

SCA_SDF_MODULE (decimator){ sca_sdf_in < double >in; sca_sdf_out < double >out;

double old_input;

void init(){ in.set_rate(2); out.set_rate(1); old_input=0; } void sig_proc(){ double input=in.read(0)/2; out.write(old_input+input); old_input=input; } SCA_CTOR (decimator){}};

1.Issues

2.SystemC-AMS Languagea. Models of Computation

b. SDF Behavioral Description

c. SDF Multi-rates

3.RF and AMS Modelinga. AMS Models

b. RF Models

4.Wireless Sensor Network Node

5.Conclusion

M. Vasilevski Laboratory LIP6, University Paris6 12

3.b RF models

Power gain NF Rin RoutIIP3

Na

input outputRout

Rin a1 = f(Power gain, Rin, Rout)

a3 = f(a1, IIP3)

Na = f(NF)

a1x+a3x³

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Input amplitude = -16.02 dBm

Power Gain = 10 dB

IIP3 = 10 dBm

NF = 30 dB

3.b RF models : IIP3 and Noise Figure Test

FFT BW = 120kHz

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X(t) = DC + I1cos(t) + I2cos(2t) + I3cos(3t) + Q1cos(t) + Q2cos(2t) + Q2cos(3t)

DC I1 I2 I3

Q1 Q2 Q3

0 2 3xBB(t) =

3Q

2Q

1Q

3I

2I

1I

DC

3.b RF models : Baseband Equivalent

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SCA_SDF_MODULE (adder){ sca_sdf_in < double >inI; sca_sdf_in < double >inQ; sca_sdf_out < double >out;... void sig_proc () { out.write (inI.read()+ inQ.read()); }...

class BB{ double DC,I1,I2,I3, Q1,Q2,Q3;... BB operator+(BB x)const{ BB z(this->DC+x.DC, this->I1+x.I1, this->I2+x.I2, this->I3+x.I3, this->Q1+x.Q1, this->Q2+x.Q2, this->Q3+x.Q3); return z; }...};

SCA_SDF_MODULE (adder){ sca_sdf_in < BB >inI; sca_sdf_in < BB >inQ; sca_sdf_out < BB >out;... void sig_proc () { out.write (inI.read()+ inQ.read()); }...

3.b RF models : Baseband Equivalent Implementation

)tsin()BA()tsin(B)tsin(A

)tcos()BA()tcos(B)tcos(A

1.Issues

2.SystemC-AMS Languagea. Models of Computation

b. SDF Behavioral Description

c. SDF Multi-rates

3.RF and AMS Modelinga. AMS Models

b. RF Models

4.Wireless Sensor Network Node

5.Conclusion

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4. Wireless Sensor Network Node

• Wireless sensor network for environmental and physical monitoring:

o Temperature, vibration, pressure, motion, polluants

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4. Wireless Sensor Network Node

A/D Converter Microcontroller

RF Transceivermodulator

decimator

Application Binary File

8.53 MHz 2.4 MHz 2.4 GHz

SystemC

SystemC-AMS

2nd orderOSR=6410 bits

RZ feedback

QPSKfc=2.4GHz

ATMEGA1288 bits

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4. Wireless Sensor Network NodeRF : QPSK 2.4 GHz

64OSR

order2nd

ADC :

S

1

S

1

DAC

decimator+

- -+

encoder demux

filter

filter

muxLNA

T

1

T

1

)tf2cos( c)tf2cos( c

)tf2sin( c )tf2sin( c

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4. Wireless Sensor Network Node : Results

RF Simulation

(2.4 GHz)

SC-AMS classical simulation

SC-AMS BB eq. RF simulation

1000 bits

transmission

63.0s 0.036s

DC offset 19.9s 0.018s

Frequency offset

24.9s 0.022s

Phase mismatch

44.4s 0.031s

Noisy channel DC offset

Frequencyoffset

Phase mismatch

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4. Wireless Sensor Network Node : Results

Settings Simulation Matlab SystemC-AMS

ADC alone OSR=64

10 bits

8.53MHz

16*1024 pts 1.6 s 0.9 s

RF alone 2.4 GHz 10e3 bits

10e7 pts RF

150.7 s classic BB

63.0 s 0.036s

2-nodestransmission

Same

settings

10e3 bits - 181.7 s

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Conclusion

•Digital and Analog-Mixed Signal systems simulation Interface with SystemC (digital simulations).

•Simulations very fast C++ based.

•Easy software programmer contribution Example of a free FFT library used for IIP3 test.