基于压缩采样结构的盲多带信号预失真模型

Blind Multiband Signal Predistortion Model Based on Compressed Sampling Structure

针对在支撑集未知的情况下重构多频带信号的问题,本文提出了一种基于压缩采样结构的盲多带信号预失真模型,即将多频输出信号视为单个宽带信号,经下变频和低通滤波器处理后,送入A-MWC(Alternate Modulated Wideband Converter)结构进行盲压缩采样,而后通过基于随机支撑挑选的变步长稀疏度自适应匹配追踪算法(Sto-VSSAMP)盲重构信号,减小反馈回路带宽,降低信号采样速率,提升重构的线性化效果。本文利用双频信号进行实验测试,结果表明该方法在提高重构信号精度的同时,功率放大器输出信号的ACPR(Adjacent Channel Power Radio)较无预失真时约改善了21 dBc,NMSE(Normalized Mean Squared Error)较传统2D-DPD(Two Dimensional Digital Predistortion)约提升了2~3 dB,获得了良好的线性化性能。

To address the problem of reconstructing multiband signals with unknown support set,this paper proposed a blind multiband signal predistortion model based on compressive sampling structure,the multiband output signal was considered as a single broadband signal,and after down conversion and low pass filter processing,it was fed into the alternate modulated wideband converter(A-MWC)structure for blind compressive sampling,the signal was then blindly reconstructed by the variable step sparsity adaptive matching pursuit algorithm based on stochastic support selection(Sto-VSSAMP).The model reduced the feedback loop bandwidth,decreased the signal sampling rate,and improved the linearization effect of the reconstruction.In this paper,experimental tests are conducted using dual-band signals,the results show that this method improves the reconstructed signal accuracy while the adjacent channel power radio of the power amplifier output signal is improved by about 21 dBc compared with that without predistortion,the normalized mean squared error is improved by about 10 dBc compared with that of the conventional two dimensional digital predistortion(2D-DPD),and good linearization performance is obtained.