射频功率放大器行为建模的多基加权记忆多项式

O. Hammi, A. Abdelrahman, A. Zerguine
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引用次数: 4

摘要

本文提出了两种多基加权记忆多项式模型,用于射频功率放大器的行为建模。在这些模型中,将传统的广义和混合记忆多项式模型的记忆多项式函数替换为其加权版本。在具有强记忆效应的功率放大器样机上进行了实验验证,该样机由20mhz LTE信号驱动,配置为1001。所提出的加权广义记忆多项式和混合记忆多项式模型,与基于记忆多项式的传统模型相比,具有更好的性能。事实上,在相同的复杂度下,NMSE提高了2 dB到3 dB,在相同的性能下,系数的数量减少了近50%。
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Multi-basis weighted memory polynomial for RF power amplifiers behavioral modeling
In this paper, two multi-basis weighted memory polynomial models are proposed for radio frequency power amplifiers' behavioral modelling. In these models, the conventional memory polynomial function of the generalized and hybrid memory polynomial models is replaced by a weighted version of it. Experimental validation was performed on a power amplifier prototype exhibiting strong memory effects, and driven by a 20 MHz LTE signal with 1001 configuration. Proposed weighted generalized memory polynomial and hybrid memory polynomial models show superior performance when compared to their memory polynomial based conventional counterparts. Indeed, an NMSE improvement of 2 dB to 3 dB is obtained for the same complexity, and a reduction of almost 50% in the number of coefficients are achieved for the same performance.
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