本文選題：數(shù)字預(yù)失真 + Volterra模型��； 參考：《北京郵電大學(xué)》2015年碩士論文

【摘要】：隨著無(wú)線(xiàn)通信系統(tǒng)的應(yīng)用范圍愈來(lái)愈廣,無(wú)線(xiàn)通信頻譜資源變得越來(lái)越擁擠,需要采用非恒包絡(luò)調(diào)制方式提高頻譜使用效率,這就需要對(duì)功率放大器進(jìn)行線(xiàn)性化處理。數(shù)字預(yù)失真因其高效率、高靈活性、低成本等優(yōu)勢(shì)成為功放線(xiàn)性化應(yīng)用的重點(diǎn)�；赩olterra級(jí)數(shù)的預(yù)失真器模型因其靈活性強(qiáng),成本廉價(jià)且實(shí)現(xiàn)結(jié)構(gòu)簡(jiǎn)單而普遍應(yīng)用。 本文主要針對(duì)基于Volterra級(jí)數(shù)的功率放大器的建模與預(yù)失真技術(shù)進(jìn)行深入研究。主要工作和創(chuàng)新點(diǎn)包括： 1.本文介紹了準(zhǔn)確復(fù)雜度減小的簡(jiǎn)化Volterra級(jí)數(shù)新模型。SV(Simplified Volterra)模型是Volterra級(jí)數(shù)模型的簡(jiǎn)化模型,當(dāng)預(yù)失真模型的非線(xiàn)性階數(shù)和記憶深度值較大時(shí),模型復(fù)雜度較大。針對(duì)SV模型系數(shù)數(shù)目較大這一缺點(diǎn),將SV模型進(jìn)行改進(jìn)為ACR-SV (Accurate Complexity-Reduced Simplified Volterra)模型。計(jì)算原始SV模型系數(shù)時(shí),SV模型的模型系數(shù)數(shù)量為模型無(wú)記憶非線(xiàn)系數(shù)與模型記憶非線(xiàn)性系數(shù)相乘,致使模型復(fù)雜度較高。而ACR-SV模型在計(jì)算模型系數(shù)數(shù)目時(shí),將模型的無(wú)記憶非線(xiàn)性與記憶非線(xiàn)性分開(kāi)考慮,ACR-SV模型的模型系數(shù)為模型無(wú)記憶非線(xiàn)性系數(shù)與記憶非線(xiàn)性系數(shù)的和,在保證了模型的精確度,減小了SV模型的模型復(fù)雜度,實(shí)驗(yàn)驗(yàn)證ACR-SV模型優(yōu)于MP (Memory Polynomial)、SV和ACR-GMP模型。 2.另外,本文又介紹了改進(jìn)廣義記憶多項(xiàng)式模型。針對(duì)于GMP (Generalized Memory Polynomial)模型的模型復(fù)雜度高的缺點(diǎn),將GMP模型改進(jìn)為MGMP (Modified Generalized Memory Polynomial)模型。MGMP模型是在GMP模型的基礎(chǔ)上,改變了GMP模型三個(gè)子模型中各個(gè)記憶深度值對(duì)應(yīng)的最大非線(xiàn)性階數(shù)的值,去除GMP模型中對(duì)模型精確度影響較小的多項(xiàng)式項(xiàng),推導(dǎo)得出了MGMP模型,并通過(guò)與MP模型與GMP模型作對(duì)比,驗(yàn)證了MGMP模型在模型復(fù)雜度和模型精確度上較MP模型和GMP模型有優(yōu)勢(shì)。
[Abstract]:With the wide application of wireless communication system, the spectrum resource of wireless communication becomes more and more crowded, which requires the use of non-constant envelope modulation to improve the efficiency of spectrum use, which requires linearization of power amplifier. Because of its high efficiency, high flexibility and low cost, digital predistortion has become the focus of power amplifier linearization application. The predistorter model based on Volterra series is widely used because of its high flexibility, low cost and simple structure. In this paper, the modeling and predistortion technology of power amplifier based on Volterra series are studied. Key areas of work and innovation include: 1. In this paper, a new simplified Volterra series model, I. e., simplified Volterra series model with reduced accurate complexity, is introduced. It is a simplified model of Volterra series model. When the nonlinear order and memory depth of the predistortion model are large, the complexity of the model is higher. Aiming at the disadvantage of large number of SV model coefficients, the SV model is improved to ACR-SV Complexity-Reduced Simplified Volterra model. When calculating the coefficients of the original SV model, the number of the coefficients of the SV model is multiplied by the non-linear coefficients of the model without memory and the nonlinear coefficient of the memory of the model, which results in the higher complexity of the model. When the ACR-SV model calculates the number of model coefficients, the model coefficients of ACR-SV model are considered as the sum of the model memoryless nonlinear coefficients and memory nonlinear coefficients separately from the memory nonlinearity, which ensures the accuracy of the model. The complexity of SV model is reduced, and the experimental results show that ACR-SV model is superior to MP memory model and ACR-GMP model. 2. In addition, this paper also introduces the improved generalized memory polynomial model. In view of the high complexity of the GMP generalized Memory model, the GMP model is improved to the MGMP modified Generalized Memory model .MGMP model is based on the GMP model. The maximum nonlinear order corresponding to each memory depth in three sub-models of GMP model is changed, and the polynomial terms in GMP model which have little influence on model accuracy are removed, and the MGMP model is derived. Compared with MP model and GMP model, MGMP model has advantages over MP model and GMP model in complexity and accuracy.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN722.75

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本文編號(hào)：1794357