改进变步长最小均方算法在组合学习结构预失真中应用的研究
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摘要
针对预失真间接学习结构易受加性噪声、模数转换(analog to digital converter,ADC)量化噪声等影响,提出一种通过设置判别门限自适应切换直接学习结构和间接学习结构的组合学习结构数字预失真方案。该方案在直接学习结构中采用最小二乘法(recursive least square,RLS)算法对参数进行快速粗估计,切换至间接学习结构时采用改进变步长最小均方(least mean square,LMS)算法进一步提取参数。分析仿真表明,组合学习结构的预失真方案其线性化性能较间接学习结构有很大提升,且在算法收敛速度基本持平的情况下有效抑制了间接学习结构中的非相关噪声。
To overcome disadvantages of additive noise and quantization noise of analog to digital converter( ADC) in the indirect learning architecture,a new composite learning architecture scheme in digital pre-distortion is proposed,which switching direct and indirect learning structure of learning structure depending on the setting appropriate threshold adaptively. In direct learning structure,the recursive least square( RLS) algorithm is used to fast estimate the parameters,and the minimum mean square( LMS) algorithm is adopted to extract the parameters when it is switched to the indirect learning structure. The simulation and analyses show that the linearization performance of the proposed scheme is improved greatly than which in the indirect learning structure. Furthermore,with two convergence rates of the algorithm equal practically it can restrain the non-correlative noises in indirect learning structure effectively.
To overcome disadvantages of additive noise and quantization noise of analog to digital converter( ADC) in the indirect learning architecture,a new composite learning architecture scheme in digital pre-distortion is proposed,which switching direct and indirect learning structure of learning structure depending on the setting appropriate threshold adaptively. In direct learning structure,the recursive least square( RLS) algorithm is used to fast estimate the parameters,and the minimum mean square( LMS) algorithm is adopted to extract the parameters when it is switched to the indirect learning structure. The simulation and analyses show that the linearization performance of the proposed scheme is improved greatly than which in the indirect learning structure. Furthermore,with two convergence rates of the algorithm equal practically it can restrain the non-correlative noises in indirect learning structure effectively.
引文
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8 Mayyas K,Momani F.An LMS adaptive algorithm with a new stepsize control equation.Journal of the Franklin Institute,2011;24(5):589-605
9 Zhang Z,Zhang D.New variable step size LMS adaptive filtering algorithm and its performance analysis.Systems Engineering and Electronics,2009;31(9):2238-2241
10 Liu Y J,Zhou J,Chen W H.A robust augmented complexity reduced generalized memory polynomial for wideband RF power amplifiers.IEEE Trans on Industrial Electronics,2014;61(5):2389-2401
2 Zhang Y,Tang W.OFDM PAPR reduction with digital amplitude predistortion.URSI GASS,Beijing:IEEE,2014:1-4
3 Zhang F,Wang Y,Ai B.Novel adaptive digital pre-distortion based on the hybrid indirect learning algorithm.2014 IEEE International Symposium on BMSB,Beijing:IEEE,2014:1-4
4 Liu Y J,Zhou J,Chen W H,et al.A robust augmented complexity reduced generalized memory polynomial for wideband RF power amplifiers.IEEE Trans Ind Electron,2014;61(5):2389-2401
5 Jessica C C,Christian F,Thomas E.A new variant of the indirect learning architecture for the linearization of power amplifiers.Eu MIC,2015 10thEuropean,Paris:IEEE,2015:444-447
6 Yao S J,Qian H,Kang K,et al.A recursive least squares algorithm with reduced complexity for digital pre-distortion linearization.IC-ASSP,2013 IEEE International Conference on,Vancouver,BC:IEEE,2013:4736-4739
7 Mandic D P.A generalized normalized gradient descent algorithm.IEEE Trans Ind Electron,2004;11(2):115-118
8 Mayyas K,Momani F.An LMS adaptive algorithm with a new stepsize control equation.Journal of the Franklin Institute,2011;24(5):589-605
9 Zhang Z,Zhang D.New variable step size LMS adaptive filtering algorithm and its performance analysis.Systems Engineering and Electronics,2009;31(9):2238-2241
10 Liu Y J,Zhou J,Chen W H.A robust augmented complexity reduced generalized memory polynomial for wideband RF power amplifiers.IEEE Trans on Industrial Electronics,2014;61(5):2389-2401