摘要
This paper proposes a Real-Valued Time-Delay Recurrent Radial Basis Function (RVTDRRBF) model suitable for dynamic modeling of the strongly nonlinear behaviors of the Doherty Power Amplifiers (DPAs). This model has four Tapped Delay Lines (TDLs), which account for the memory effect of the DPA. The structure of the RVTDRRBF model is simpler than the traditional FeedForward Neural Networks (FFNNs) model. Weights and centers of the proposed model can be resolved by the Orthogonal Least Square (OLS) and Singular Value De-composition (SVD) algorithm. A three-carrier Wideband Code Division Multiple Access (WCDMA) signal is taken as the test signal. The simulation results in frequency-domain and time-domain for a DPA with 51 dBm output illustrate a good agreement between the RVTDRRBF model and measurement data. Moreover, comparing the Normalized Mean Square Error (NMSE) of RVTDRRBF model, memory polynomial model and RVTDRBF model, it can be noticed that the proposed RVTDRRBF model is more accurate than the RVTDRBF model and the memory polynomial model in modeling the strong dynamic nonlinearity of the DPAs.
This paper proposes a Real-Valued Time-Delay Recurrent Radial Basis Fhnction （RVTDRRBF） model suitable for dynamic modeling of the strongly nonlinear behaviors of the Doherty Power Amplifiers （DPAs）. This model has four Tapped Delay Lines （TDLs）, which account for the memory effect of the DPA. The structure of the RVTDRRBF model is simpler than the traditional FeedForward Neural Networks （FFNNs） model. Weights and centers of the proposed model can be resolved by the Orthogonal Least Square （OLS） and Singular Value De-composition （SVD） algorithm. A three-carrier Wideband Code Division Multiple Access （WCDMA） signal is taken as the test signal. The simulation results in frequency-domain and time-domain for a DPA with 51 dBm output illustrate a good agreement between the RVTDRRBF model and measurement data. Moreover, comparing the Normalized Mean Square Error （NMSE） of RVTDRRBF model, memory polynomial model and RVTDRBF model, it can be noticed that the proposed RVTDRRBF model is more accurate than the RVTDRBF model and the memory polynomial model in modeling the strong dynamic nonlinearity of the DPAs.
基金
Supported by the National Science and Technology Major Project of China (2010ZX03007-003-04)
the National Natural Science Foundation of China (No. 61171040)
the Key Project of International Cooperation of the Provincial Science and Technology Major Projects of Zhejiang (2010C14007)
the Provincial Natural Science Foundation of Zhejiang (Y1101270)
the Natural Science Foundation of Ningbo (2011A610188)
Key Project of International Scientific and Technical Cooperation of Yunnan (2009AC-010)
Excellent Papers Engagement Fund of Ningbo University (PY20100004)
