Modeling and linearizing broad-band power amplifier based onreal and complex-valued hybrid time-delay neural network

A new real and complex-valued hybrid time-delay neural network(TDNN)is proposed for modeling and linearizing the broad-band power amplifier(BPA). The neural network includes the generalized memory effect of input signals, complex-valued input signals and the fractional order of a complex-valued input signal module, and, thus, the modeling accuracy is improved significantly. A comparative study of the normalized mean square error(NMSE)of the real and complex-valued hybrid TDNN for different spread constants, memory depths, node numbers, and order numbers is studied so as to establish an optimal TDNN as an effective baseband model, suitable for modeling strong nonlinearity of the BPA. A 51-dBm BPA with a 25-MHz bandwidth mixed test signal is used to verify the effectiveness of the proposed model. Compared with the memory polynomial(MP)model and the real-valued TDNN, the real and complex-valued hybrid TDNN is highly effective, leading to an improvement of 5 dB in the NMSE. In addition, the real and complex-valued hybrid TDNN has an improvement of 0.6 dB over the generalized MP model in the NMSE. Also, it has better numerical stability. Moreover, the proposed TDNN presents a significant improvement over the real-valued TDNN and the MP models in suppressing out-of-band spectral regrowth.