1. 1 IWS-IEEE.org Visit: Behavioral Modeling of Power Amplifier with Long Term Memory Effects using Recurrent Neural Networks Chuan Zhang, Shuxia Yan, Qi-Jun Zhang, Jian-Guo Ma WE1B-5 School of Electronics and Information Engeering Tianjin University, China

2. 2 Outline 1. Background 2. Recurrent neural network technique for PA 3. A PA example modeled by RNN and DNN 4. Conclusion

3. 1. Background

4. 4 ANN modeling technique ANN can represent multi-dimensional nonlinear input-output problems ANN model is fast and compact ANN becomes a logical choice as a basic part in an automated modeling algorithm

5. 5 Software support Training data Power Amplifiers Agilent ADS NeuroModeler Agilent ADS Circuit/ System Simulation/ Optimization Fast behavior models

6. 6 PA example

7. 7 Behavioral modeling of PA 1. Dynamic neural network (DNN) 2. Recurrent neural network (RNN)

8. 8 DNN for modeling Short-term memory effect Short-term memory effect DNN Vin Vout

9. 9 DNN for modeling Short-term memory effect

10. 10 DNN for modeling long-term memory effect Long-term memory effect DNN VinVin_LVout_L Vout

11. 11 Slow-changing signals

12. 12 DNN for modeling long-term memory effect

13. 13 Gain comparison between DNN model and original circuit

14. 14 IMD3 comparison between DNN long- term model and original circuit

15. 2. Recurrent neural network (RNN)

16. 16 RNN for modeling Short-term memory effect Short-term memory effect RNN Vin Vout

17. 17 RNN for modeling Short-term memory effect V in (t-τ)V in (t-2τ)V out (t-τ) V out (t-2τ)

18. 18 RNN for modeling long-term memory effect Long-term memory effect RNN VinVin_LVout_L Vout

19. 19 RNN for modeling long-term memory effect Τ=k*τ

20. 20 Equivalent circuit of the RNN model

21. 3. A PA example modeled by RNN and DNN

22. 22 My PA example modeled by RNN and DNN

23. 23 Table comparison with DNN and RNN

24. 24 Gain comparison between DNN model,RNN model and original circuit

25. 25 IMD3 comparison between DNN model,RNN model and original circuit

26. 4. Conclusion

27. 27 Conclusion We have presented an RNN method for modeling nonlinear PA circuit with memory effects. By including new slow changing signals extracted from the PA input and output signals, the proposed formulation of RNN can effectively represent long term memory effects.

28. Thank you !