behavioral modeling of power amplifier using dnn and rnn
DESCRIPTION
Behavioral Modeling of Power Amplifier using DNN and RNN. Zhang Chuan. 1. 2. 3. DNN and RNN Modeling using new transistor. Next Work. Review. Outline. 1. Review. Review. Power amplifier. Memory effect. Short-term memory effect Long-term memory effect. Neural Network Modeling. - PowerPoint PPT PresentationTRANSCRIPT
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