TJU

Behavioral Modeling of Power Amplifier using DNN

and RNN

Zhang Chuan

Outline

Review1

DNN and RNN Modeling using new transistor2

Next Work3

ReviewReview1

Power amplifier

Memory effect

Short-term memory effect

Long-term memory effect

Long-term memory effect

Neural Network Modeling

Vin

Vin_L

Vout_L

Vout

Vin

Vout

Long-term memory effect example

Neural Network Modeling

Vin

Vout

Vin_L

Vout_L

Short-term DNN structure

Neural Network Modeling

Long-term DNN structure

Neural Network Modeling

Short-term RNN structure

Vin(t-τ) Vin(t-2τ)

Vout(t-τ) Vout(t-2τ)

Long-term RNN structure

Т Т

Τ=nτ

Vout(t-τ) Vout_L(t)

Vout_L(t-Τ)

Vin(t-τ) Vin_L(t)

Vout_L(t-Τ)

_ _ _

_ _ _

( ) ( ), ( ), , ( ),( ), ( ), , ( ),

( ), ( ), , ( ),

( ), , ( )

(

)

out in in in

in L in L in L

out L out L out L

out out

Т Т

Т Т

v t f v t v t v t mv t v t v t m

v t v t v t n

v t v t n

Short-term DNN vs RNN

DNNderivative unit: both 2harmonics: 3hidden neurons: 30training data:Pin:0~24 dBm step:2dBm freq: 850~900 MHz step: 5MHz test data:Pin: 1~23 dBm step: 2dBmfreq: 852.5~897.5 MHz step:5MHz training error:Time-domain : 0.0174%Freq-domain : 0.9246%test error:Time-domain : 0.018%Freq-domain : 1.1514%

RNNdelay unit: both 2harmonics: 3hidden neurons: 30training data:Pin:0~24 dBm step:2dBm freq: 850~900 MHz step: 5MHz test data:Pin: 1~23 dBm step: 2dBmfreq: 852.5~897.5 MHz step:5MHz training error:FFNN : 0.019%RNN : 0.1133%test error:FFNN : 0.0159%RNN : 0.125%

Short-term Result(DNN vs RNN)

Long-term DNN vs RNN DNNderivative unit: Vin:2 Vin_L:2 Vout_L:2Iin:1 Vout:2harmonics: both 5hidden neurons: 55training data:Pin: 0~6 dBm step:2dBm fspacing: 5~50 MHz step: 5MHz test data:Pin: 1~5 dBm step: 2dBmfreq: 7.5~47.5 MHz step:5MHz training error:Time-domain : 0.0449%Freq-domain : 1.7352%test error:Time-domain : 0.2653%Freq-domain : 2.1134%

RNNdelay unit: Vin:2 Vin_L:2 Vout_L:2Iin:1 Vout:2harmonics: both 5hidden neurons: 55training data:Pin: 0~6 dBm step:2dBm fspacing: 5~50 MHz step: 5MHz test data:Pin: 1~5 dBm step: 2dBmfreq: 7.5~47.5 MHz step:5MHz training error:FFNN : 0.0363%RNN : 0.0627%test error:FFNN : 0.0418%RNN : 0.0782%

Long-term Result(DNN vs RNN)

DNN and RNN Modeling using new transistor2

Whole PA circuit

New PA example using freescale transistor

New PA example using freescale transistor(in ADS)

Short-term comparison (DNN vs RNN)

DNNderivative unit: 3 3 2harmonics: 5hidden neurons: 30training data:Pin:0~32 dBm step:2dBm freq: 2.6~2.65 GHz step: 10MHz test data:Pin: 1~31 dBm step: 2dBmfreq: 2.605~2.645 MHz step:10MHz training error:Time-domain : 0.0057%Freq-domain : 0.8436%test error:Time-domain : 0.0062%Freq-domain : 0.9514%

RNNderivative unit: 3 3 2harmonics: 5hidden neurons: 30training data:Pin:0~32 dBm step:2dBm freq: 2.6~2.65 GHz step: 10MHz test data:Pin: 1~31 dBm step: 2dBmfreq: 2.605~2.645 MHz step:10MHz training error:FFNN : 0.0472%RNN : 0.0113%test error:FFNN : 0.0291%RNN : 0.0335%

Short-term memory result

Long-term DNN vs RNN DNNderivative unit: Vin:2 Vin_L:2 Vout_L:2Iin:1 Vout:2harmonics: both 5hidden neurons: 40training data:Pin: 16~22 dBm step:2dBm fspacing: 150~370 MHz step: 20MHz test data:Pin: 17~21 dBm step: 2dBmfspacing: 160~360 MHz step:20MHz training error:Time-domain : 0.0337%Freq-domain : 1.3751%test error:Time-domain : 0.1253%Freq-domain : 2.6134%

RNNderivative unit: Vin:2 Vin_L:2 Vout_L:2Iin:1 Vout:2harmonics: both 5hidden neurons: 40training data:Pin: 16~22 dBm step:2dBm fspacing: 150~370 MHz step: 20MHz test data:Pin: 17~21 dBm step: 2dBmfspacing: 160~360 MHz step:20MHz training error:FFNN : 0.0036%RNN : 0.0534%test error:FFNN : 0.0048%RNN : 0.0626%

Long-term memory result(fine model)

DNN two lines training result

DNN two lines test result

RNN two lines training result

RNN two lines test result

Long-term DNN vs RNN DNNderivative unit: Vin:2 Vin_L:2 Vout_L:2Iin:1 Vout:2harmonics: both 5hidden neurons: 25training data:Pin: 16~18 dBm step:2dBm fspacing: 150~370 MHz step: 30MHz test data:Pin: 17 dBm fspacing: 160~340 MHz step:30MHz 

RNNderivative unit: Vin:2 Vin_L:2 Vout_L:2Iin:1 Vout:2harmonics: both 5hidden neurons: 25training data:Pin: 16~18 dBm step:2dBm fspacing: 150~370 MHz step: 30MHz test data:Pin: 17 dBm fspacing: 160~340 MHz step:30MHz 

L_7_2_td4

Use less number of training data

Test using more data

Next Work3

Next work

I’ll figure out:

Long-term memory effects modeling, choose a precise size of data and reduced DNN and RNN structure to get a good result.

TJU