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Behavioral Modeling of Power Amplifier using DNN and RNN

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.

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Behavioral Modeling of Power Amplifier using DNN and RNN

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  1. Behavioral Modeling of Power Amplifier using DNN and RNN Zhang Chuan

  2. 1 2 3 DNN and RNN Modeling using new transistor Next Work Review Outline

  3. 1 Review Review

  4. Power amplifier

  5. Memory effect • Short-term memory effect • Long-term memory effect

  6. Neural Network Modeling Long-term memory effect Vout Vin Vin Vin_L Vout Vout_L

  7. Long-term memory effect example Neural Network Modeling Vin Vin_L Vout_L Vout

  8. Neural Network Modeling Short-term DNN structure

  9. Neural Network Modeling Long-term DNN structure

  10. Short-term RNN structure Vout(t-2τ) Vout(t-τ) Vin(t-τ) Vin(t-2τ)

  11. Long-term RNN structure Vout_L(t-Τ) Vout_L(t-Τ) Vout(t-τ) Vout_L(t) Vin_L(t) Vin(t-τ) Τ=nτ

  12. Short-term DNN vs RNN RNN delay unit: both 2 harmonics: 3 hidden neurons: 30 training data: Pin:0~24 dBm step:2dBm freq: 850~900 MHz step: 5MHz test data: Pin: 1~23 dBm step: 2dBm freq: 852.5~897.5 MHz step:5MHz training error: FFNN : 0.019% RNN : 0.1133% test error: FFNN : 0.0159% RNN : 0.125% DNN derivative unit: both 2 harmonics: 3 hidden neurons: 30 training data: Pin:0~24 dBm step:2dBm freq: 850~900 MHz step: 5MHz test data: Pin: 1~23 dBm step: 2dBm freq: 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%

  13. Short-term Result(DNN vs RNN)

  14. Long-term DNN vs RNN DNN derivative unit: Vin:2 Vin_L:2 Vout_L:2 Iin:1 Vout:2 harmonics: both 5 hidden neurons: 55 training data: Pin: 0~6 dBm step:2dBm fspacing: 5~50 MHz step: 5MHz test data: Pin: 1~5 dBm step: 2dBm freq: 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% RNN delay unit: Vin:2 Vin_L:2 Vout_L:2 Iin:1 Vout:2 harmonics: both 5 hidden neurons: 55 training data: Pin: 0~6 dBm step:2dBm fspacing: 5~50 MHz step: 5MHz test data: Pin: 1~5 dBm step: 2dBm freq: 7.5~47.5 MHz step:5MHz training error: FFNN : 0.0363% RNN : 0.0627% test error: FFNN : 0.0418% RNN : 0.0782%

  15. Long-term Result(DNN vs RNN)

  16. 2 DNN and RNN Modeling using new transistor

  17. Whole PA circuit

  18. New PA example using freescale transistor

  19. New PA example using freescale transistor(in ADS)

  20. Short-term comparison (DNN vs RNN) RNN derivative unit: 3 3 2 harmonics: 5 hidden neurons: 30 training data: Pin:0~32 dBm step:2dBm freq: 2.6~2.65 GHz step: 10MHz test data: Pin: 1~31 dBm step: 2dBm freq: 2.605~2.645 MHz step:10MHz training error: FFNN : 0.0472% RNN : 0.0113% test error: FFNN : 0.0291% RNN : 0.0335% DNN derivative unit: 3 3 2 harmonics: 5 hidden neurons: 30 training data: Pin:0~32 dBm step:2dBm freq: 2.6~2.65 GHz step: 10MHz test data: Pin: 1~31 dBm step: 2dBm freq: 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%

  21. Short-term memory result

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

  23. Long-term memory result(fine model)

  24. DNN two lines training result

  25. DNN two lines test result

  26. RNN two lines training result

  27. RNN two lines test result

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

  29. L_7_2_td4

  30. Use less number of training data

  31. Test using more data

  32. 3 Next Work

  33. 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.

  34. Thank You !

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