An Improved Pre-Distortion Algorithm Based On Indirect Learning Architecture for Nonlinear Power Amplifiers

With the rapid development of wireless communication techniques, the main limitations of wireless communications lie in spectrum resource constraints and nonlinearity of high amplifier power (HPA). Albeit exploring high-order modulation improves the spectrum efficiency significantly, however, the nonlinear distortion seriously degrades the performance especially when nonlinearity sensitive high-order non-constant envelop modulation signal pass through the saturate regime of HPA. Due to the good performance in linearization, adaptive baseband digital pre-distortion has been widely developed and applied. Particularly, the optimization of the parameters for adaptive pre-distortion algorithm has a positive impact on linearization. In this paper, an improved algorithm is proposed to determine the optimum step by taking maximum error vector magnitude EVM improvement as the objective function. And thus the nonlinearity of power amplifier is compensated with indirect learning architecture and optimum step adaptive algorithm. Simulation results demonstrate that the constellations are well compensated and out-band power spectrum regenerations are significantly suppressed, which yields a satisfactory system performance linearization.