Behavioral Modeling of Power Amplifier using DNN and RNN
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Transcript of Behavioral Modeling of Power Amplifier using DNN and RNN
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Behavioral Modeling of Power Amplifier using DNN
and RNN
Zhang Chuan
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Outline
Review1
DNN and RNN Modeling using new transistor2
Next Work3
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ReviewReview1
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Power amplifier
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Memory effect
Short-term memory effect
Long-term memory effect
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Long-term memory effect
Neural Network Modeling
Vin
Vin_L
Vout_L
Vout
Vin
Vout
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Long-term memory effect example
Neural Network Modeling
Vin
Vout
Vin_L
Vout_L
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Short-term DNN structure
Neural Network Modeling
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Long-term DNN structure
Neural Network Modeling
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Short-term RNN structure
Vin(t-τ) Vin(t-2τ)
Vout(t-τ) Vout(t-2τ)
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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
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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%
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Short-term Result(DNN vs RNN)
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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%
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Long-term Result(DNN vs RNN)
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DNN and RNN Modeling using new transistor2
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Whole PA circuit
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New PA example using freescale transistor
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New PA example using freescale transistor(in ADS)
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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%
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Short-term memory result
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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%
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Long-term memory result(fine model)
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DNN two lines training result
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DNN two lines test result
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RNN two lines training result
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RNN two lines test result
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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
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L_7_2_td4
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Use less number of training data
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Test using more data
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Next Work3
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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.
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