外文翻譯--在數(shù)字通信中多速率信號(hào)處理的概念

1、<p><b>　　B.1 中文翻譯</b></p><p>　　在數(shù)字通信中多速率信號(hào)處理的概念</p><p>　　選自——加州理工大學(xué)帕薩迪納（加利福尼亞）博士論文</p><p>　　多抽樣頻率系統(tǒng)通常是被運(yùn)用在處理數(shù)字信號(hào)方面的。他們的功能是用來(lái)改變離散時(shí)間信號(hào)的抽樣頻率,從而通過(guò)這種方式來(lái)達(dá)到增加或減小信號(hào)采樣頻率的目的。

2、多抽樣頻率系統(tǒng)中最主要的部分在于多抽樣率信號(hào)的處理, 主要是用于多樣濾波理論的用途中。</p><p>　　他們?cè)谔幚淼母鞣N不同的標(biāo)準(zhǔn)信號(hào)方面時(shí)，是信號(hào)分析、處理、壓縮等的必要的技術(shù)手段。在20世紀(jì)最后十年期間，然而，他們逐漸地被廣泛應(yīng)用在信號(hào)處理新出現(xiàn)的區(qū)域里,以及相關(guān)數(shù)字信號(hào)領(lǐng)域里。</p><p>　　本論題的主要的貢獻(xiàn)是比較好的理解面向?qū)ο蟮亩喑闃宇l率系統(tǒng)和他們?cè)诂F(xiàn)代的通信模式下的

3、使用。最后, 我們首要研究的是特定的多抽樣頻率系統(tǒng)結(jié)構(gòu)。這一性質(zhì)稱(chēng)為雙正交關(guān)系，并且代表了一種術(shù)語(yǔ)，這種術(shù)語(yǔ)用以描述這一類(lèi)濾波器的相鄰數(shù)據(jù)的處理過(guò)程。在論題中我們所討論的重點(diǎn)放在對(duì)簡(jiǎn)單的周期信號(hào)進(jìn)行擴(kuò)展 (MIMO 雙正交的對(duì)象) 和非整數(shù)比的抽樣頻率問(wèn)題.(少數(shù)的雙正交的對(duì)象)。</p><p>　　從此而發(fā)展引出的一些主要結(jié)果在這里能夠比較好的解釋雙正交的對(duì)象之間的關(guān)系。這些包括有限沖擊響應(yīng)雙正交對(duì)象存在的情

4、況。</p><p>　　我們建立的一個(gè)主要的結(jié)論在于在一些通常平穩(wěn)的情況下, MIMO 和少量的雙正交的對(duì)象存在。而且，當(dāng)他們存在的時(shí)候，有限沖擊響應(yīng)的解決方法不是唯一的。</p><p>　　我們開(kāi)發(fā)的參數(shù)化的解決方案，使在給定應(yīng)用中尋找最佳對(duì)象變得更實(shí)際，更加可分析化，而且這證明在對(duì)象的核心處理中是非常有用的，即，在接收器部分保持信道均衡，從而進(jìn)行信號(hào)采樣的數(shù)字化通信。被抽取樣品的信

5、號(hào)在通常情況下頻率要比由發(fā)射器所提供的抽取的樣品要高，所以要使用一些靈活的方法使其均衡。</p><p>　　一個(gè)好的信道能夠另上下行的數(shù)據(jù)均衡，有助于消除信道傳播中的失真的信號(hào)，有助于一個(gè)重建信號(hào)方面進(jìn)行通道增殖，但是不會(huì)做出擴(kuò)大通道噪音的犧牲。這一個(gè)部分是用來(lái)制定和解決后面的面向?qū)ο蟮脑碓O(shè)計(jì)問(wèn)題。這樣使其表現(xiàn)出均衡的速率，然后進(jìn)行均衡計(jì)算機(jī)模擬方法。這些調(diào)查結(jié)果顯現(xiàn)出的結(jié)論是，哪一種傳輸方法的表現(xiàn)能夠在改良

6、并減少損耗地同時(shí)，并不增加接收方面的費(fèi)用。</p><p>　　多抽樣頻率數(shù)字信號(hào)處理器在上述提供的自由的傳輸制度服務(wù)之外還包括接收部分的設(shè)計(jì),此外的重點(diǎn)是，分類(lèi)多抽樣頻率結(jié)構(gòu)用在發(fā)端，是為了要減少多余的流水?dāng)?shù)據(jù)。一個(gè)多余的通?？捎糜诖龠M(jìn)均衡程序上特定傳送的信號(hào)。如果信道未知,這個(gè)程序能夠幫助監(jiān)視信道;如果信道有問(wèn)題，那么就還需要其他的幫助。避免接收時(shí)出現(xiàn)擴(kuò)大的白噪音,等等。</p><p&g

7、t;　　在第二論題的這一部份中, 我們?cè)谶@方面的焦點(diǎn)主要放在群體的多抽樣頻率系統(tǒng)上,源自一些他們的方法，而且向讀者介紹一些有疑問(wèn)的傳輸系統(tǒng)的發(fā)展。</p><p>　　我們首先考慮以循環(huán)的前綴插入的形式來(lái)實(shí)現(xiàn)的傳輸系統(tǒng)。這樣的系統(tǒng)例如離散的多形式(DMT) 和直角頻率多工法 (OFDM) 系統(tǒng)。循環(huán)的前綴插入能夠在非重疊頻率的特定的數(shù)字中，有效幫助兩個(gè)要求不對(duì)等的系統(tǒng)間進(jìn)行通信。我們認(rèn)識(shí)到，在這樣的系統(tǒng)中的信號(hào)，

8、是為了要使資源在不同的頻域中得到完全橫向的利用。我們的最終目標(biāo)是借由，在接收部分將干擾減到最少的方式，改善全部的系統(tǒng)表現(xiàn)。我們一般解決的是，只存在白噪聲的理想通道環(huán)境中的方法配置。</p><p>　　最后,我們研究了不同的傳輸制度對(duì)感應(yīng)信號(hào)的影響, 即,使用者多基于碼分多址 (CDMA).的系統(tǒng)。我們特別關(guān)注的一類(lèi)特殊的碼分多址系統(tǒng)稱(chēng)為`相互正交usercode接收機(jī)。這些系統(tǒng)使用多種不同的傳輸途徑，在接收部分

9、使用離散方式進(jìn)行整合。這種方法也保證了迫零進(jìn)衡器中零點(diǎn)位置的存在, 這些均衡的表現(xiàn)可能在他們的設(shè)計(jì)中借由開(kāi)發(fā)固有的特性得到進(jìn)一步改良。我們應(yīng)該如何找到最好的方式解決零極點(diǎn)的問(wèn)題，并且在接收部分增加抽取樣品策略的替代或選擇方式以提高工作效率？當(dāng)環(huán)境干擾改善并使信號(hào)更清晰的時(shí)候，我們的方法能夠保留原有系統(tǒng)的離散特性。</p><p><b>　　第 1 章 介紹</b></p>

10、<p>　　基于多抽樣率的數(shù)字信號(hào)處理 (DSP) 在傳統(tǒng)上的應(yīng)用是在濾波器，上文提到的有 [61] 、 [13] 、 [50] 和小節(jié) [31],[72]。這些在信號(hào)分解,分析、模型和重建中的作用是非常重要的。多數(shù)信號(hào)處理的部分會(huì)比想像中困難是因?yàn)闆](méi)有使用數(shù)字取樣理論。這對(duì)聲音、影像和圖像壓縮的處理尤其實(shí)用,數(shù)字信號(hào)的聲音處理, 信號(hào)壓縮, 適合于統(tǒng)計(jì)的信號(hào)處理等。然而，最近多抽樣率數(shù)字信號(hào)處理被發(fā)現(xiàn)在數(shù)字信號(hào)處理方面的要求

11、逐漸增加。多抽樣率信號(hào)處理在現(xiàn)代的多數(shù)傳輸制度中是決定性的部分, 舉例來(lái)說(shuō), (DMT), (DSL) 和頻分多址(OFDM) 系統(tǒng)和一般的濾波器中都有應(yīng)用, 僅僅是名字有些不同。感興趣的讀者在這些主題上提供了很多的叁考, 像是 [7]-[9], [17]-[18] 、 [27] 、 [30] 、 [49] 、 [64] 、 [89], 等等。</p><p>　　這一個(gè)論題的重要性在于在數(shù)字信號(hào)方面進(jìn)一步的對(duì)多

12、抽樣率系統(tǒng)的理解。</p><p>　　最后, 我們將介紹一些新的信號(hào)處理的觀念和它們的可行性。我們?cè)谥贫ㄊ褂枚喑闃勇史椒▽W(xué)時(shí)也尤其考慮了傳輸?shù)囊恍┲匾膯?wèn)題。在這個(gè)介紹性的章節(jié)中, 我們?cè)谡薪榻B了多抽樣率系統(tǒng)的概況并且介紹一些公式、符號(hào)和用語(yǔ)等在論題的其它部分中的證明中需要用到的知識(shí)。每種做法都是主要地服務(wù)于使現(xiàn)在的本文盡可能包含和介紹詳盡的有關(guān)知識(shí)。論題的一些部份, 尤其那些有關(guān)與對(duì)象的雙正交理論和基于它

13、們而引申出的結(jié)論都得到了相當(dāng)廣泛的認(rèn)可,在一些比較容易理解和比較理想化的系統(tǒng)中更多的可以用到關(guān)于傳輸方式的多抽樣率理論。</p><p>　　對(duì)于一個(gè)比較廣泛的選擇都需要有廣泛的變換范圍,例如, [71] 、 [18] 、 [19] 、 [39] 、 [38] 、 [53], 等等。</p><p>　　1.1 多抽樣率系統(tǒng) 1.1.1數(shù)據(jù)基礎(chǔ)</p><p>　

14、　在數(shù)據(jù)傳輸過(guò)程中進(jìn)行傳輸?shù)男盘?hào)通常是離散的信號(hào)序列x(n) 、 y(n)等等，離散序列 x(n) 時(shí)常是藉由對(duì)連續(xù)的時(shí)間信號(hào)xc(t)抽取樣品得到。多數(shù)的原始信號(hào) (如到達(dá)我們的耳朵的聲音信號(hào)或到達(dá)我們的眼睛的光信號(hào)) 都是連續(xù)的時(shí)間信號(hào)。然而, 為了要對(duì)它們進(jìn)行傳輸和數(shù)字信號(hào)處理, 它們需要被抽取樣品并且進(jìn)行數(shù)字信號(hào)轉(zhuǎn)換。這轉(zhuǎn)變也包括信號(hào)量化,也就是,使信號(hào)由連續(xù)的時(shí)間信號(hào)變?yōu)殡x散, 然而在它們也能夠被還原為近似于原始信號(hào)的合成信號(hào)

15、 x(n)。</p><p>　　在單一化頻率領(lǐng)域考慮原始信號(hào)的處理即信號(hào)和系統(tǒng)。一般意義上對(duì) x(n) 的變換主要有它的 z-轉(zhuǎn)換 X(z) 和離散時(shí)間傅立葉轉(zhuǎn)換 X.(O')。z-轉(zhuǎn)換是定義當(dāng)做 X(z)= E _. x(n)z-"', 和 X(ej") 即在單位圓周z = e3上評(píng)估的 X(z)".</p><p>　　多抽樣率數(shù)字信號(hào)處

16、理系統(tǒng)通常有三個(gè)基本的組成部分, 進(jìn)行離散時(shí)間信號(hào) x(n)的處理.即線性時(shí)不變(LTI)系統(tǒng)，抽取和插值。一個(gè) LTI 過(guò)濾器, 像那一在圖1.1 顯示, 用它的沖激響應(yīng) h(n), 或它的 z 變換 (也被稱(chēng)為移動(dòng)功能) H(z)來(lái)表示。M-折層抽取的例子和插值為 M=2 如圖1.2 所示。信號(hào)的取樣率在插值后的輸出是 M 值比較高的超過(guò)它的輸入的取樣率, 當(dāng)進(jìn)行相反的操作時(shí)即為進(jìn)行抽取。那就是為什么系統(tǒng)包含插值和抽取就被稱(chēng)為是多抽

17、樣率系統(tǒng)。</p><p>　　圖1.2 在時(shí)域和頻域中表示了插值和抽取。</p><p>　　在圖1.1 and 1.2 被顯示的系統(tǒng)在信號(hào)上的操作被稱(chēng)為單信號(hào)輸入輸出 (SISO) 制度。對(duì)矢量信號(hào)的情況的延長(zhǎng)相當(dāng)直:插值和取樣是分開(kāi)地在每個(gè)元素上進(jìn)行。那對(duì)應(yīng)的矢量序列取樣/插值是在圖中可以表現(xiàn)出來(lái)。在圖1.3 這是為矢量插值的示范過(guò)程。LTI 系統(tǒng)在矢量上操作信號(hào)叫做多輸入-倍數(shù)的輸

18、出 (MIMO) 制度并且由它們表示的點(diǎn)陣 (可能矩形) 表示出了 H(z).</p><p>　　1.1.2 一些多抽樣率定義和公式</p><p>　　矢量信號(hào)有時(shí)是從被停滯的對(duì)應(yīng)的連續(xù)信號(hào)獲得的。相反地, 連續(xù)信號(hào)能是從離散的信號(hào)中恢復(fù)的。停滯/不停滯操作能定義使用延遲或進(jìn)步 [61], 這樣就引申出二種相似的定義。一個(gè)定義是這些操作的方法在圖1.4 被顯示，當(dāng)另一個(gè)信號(hào)緊密的時(shí)候

19、藉由抽樣率的轉(zhuǎn)變獲得延遲或進(jìn)步的操作。改為圖畫(huà)完全的延遲/進(jìn)步鏈結(jié)構(gòu), 我們時(shí)常使用那單一化區(qū)段記號(hào)法記做如圖1.4。它通常很清楚從上下文中判斷出二種不同的定義。</p><p><b>　　B.2 英文原文</b></p><p>　　Multirate systems are building blocks commonly used in digital sig

20、nal processing (DSP). Their function is to alter the rate of the discrete-time signals, which is achieved by adding or deleting a portion of the signal samples. Multirate systems play a central role in many areas of sign

21、al processing, such as filter bank theory and multiresolution theory. They are essential in various standard signal processing techniques such as signal analysis, denoising, compression and so forth. During the las</p

22、><p>　　The main contribution of this thesis is aimed towards better understanding of multirate systems and their use in modern communication systems. To this end, we first study a property of linear systems app

23、earing in certain multirate structures. This property is called biorthogonal partnership and represents a terminology introduced recently to address a need for a descriptive term for such class of filters. In the thesis

24、we especially focus on the extensions of this simple idea to the case of vect</p><p>　　Some of the main results developed here pertain to a better understanding of the biorthogonal partner relationship. Thes

25、e include the conditions for the existence of stable and of finite impulse response (FIR) biorthogonal partners. A major result that we establish states that under some generally mild conditions, MIMO and fractional bior

26、thogonal partners exist. Moreover, when they exist, FIR solutions are not unique. We develop the parameterization of FIR solutions, which makes the search for t</p><p>　　While the multirate DSP in the aforem

27、entioned communication systems serves to provide additional degrees of freedom in the design of the receiver, another important class of multirate structures is used at the transmitter side in order to introduce the redu

28、ndancy in the data stream. This redundancy generally serves to facilitate the equalization process by forcing certain structure on the transmitted signal. If the channel is unknown, this procedure helps to identify it; i

29、f the channel is ill-co</p><p><b>　　V</b></p><p>　　A void severe noise amplification at the receiver, and so forth. In the second part of the thesis, we focus on this second group of

30、 multirate systems, derive some of their properties and introduce certain improvements of the communication systems in question.</p><p>　　We first consider the transmission systems that introduce the redunda

31、ncy in the form of a cyclic prefix. The examples of such systems include the discrete multitone (DMT) and the orthogonal frequency division multiplexing (OFDM) systems. The cyclic prefix insertion helps to effectively di

32、vide the channel in a certain number of nonoverlaping frequency bands. We study the problem of signal precoding in such systems that serves to adjust the signal properties in order to fully take advantage of the</p>

33、;<p>　　Finally, we study a different class of communication systems with induced signal redundancy, namely, the multiuser systems based on code division multiple access (CDMA). We specifically focus on the special

34、 class of CDMA systems called `a mutually orthogonal usercode receiver' (AMOUR). These systems use the transmission redundancy to facilitate the user separation at the receiver regardless of the (different) communica

35、tion channels. While the method also guarantees the existence of the zero-forc</p><p>　　Chapter 1 Introduction</p><p>　　The theory of multirate digital signal processing (DSP) has traditionally

36、been applied to the contexts of filter banks [61], [13], [50] and wavelets [31], [72]. These play a very important role in signal decomposition, analysis, modeling and reconstruction. Many areas of signal processing woul

37、d be hard to envision without the use of digital filter banks. This is especially true for audio, video and image compression, digital audio processing, signal denoising, adaptive and statistical signal pro</p>&l

38、t;p>　　This thesis presents a contribution to further understanding of multirate systems and their significance in digital communications. To that end, we introduce some new signal processing concepts and investigate t

39、heir properties. We also consider some important problems in communications especially those that can be formulated using the multirate methodology. In this introductory chapter, we give a brief overview of the multirate

40、 systems and introduce some identities, notations and terminology tha</p><p>　　1.1 Multirate systems 1.1.1 Basic building blocks</p><p>　　The signals of interest in digital signal processing are

41、 discrete sequences of real or complex numbers denoted by x(n), y(n), etc. The sequence x(n) is often obtained by sampling a continuous-time signal xc(t). The majority of natural signals (like the audio signal reaching o

42、ur ears or the optical signal reaching our eyes) are continuous-time. However, in order to facilitate their processing using DSP techniques, they need to be sampled and converted to digital signals. This conversion also

43、incl</p><p>　　Signal processing analysis is often simplified by considering the frequency domain representation of signals and systems. Commonly used alternative representations of x(n) are its z-transform X

44、 (z) and the discrete-time Fourier transform X (O'). The z-transform is defined as X(z) = E _. x(n)z-"', and X (ej") is nothing but X(z) evaluated on the unit circle z = e3".</p><p>　　

45、Multirate DSP systems are usually composed of three basic building blocks, operating on a discrete-time signal x(n). Those are the linear time invariant (LTI) filter, the decimator and the expander. An LTI filter, like t

46、he one shown in Fig.1.1, is characterized by its impulse response h(n), or equivalently by its z-transform (also called the transfer function) H(z). Examples of the M-fold decimator and expander for M = 2 are shown in Fi

47、g.1.2. The rate of the signal at the output of an expander i</p><p>　　The systems shown in Figs.1.1 and 1.2 operate on scalar signals and thus are called single input-single output (SISO) systems. The extens

48、ions to the case of vector signals are rather straightforward: the decimation and the expansion are performed on each element separately. The corresponding vector sequence decimators/expanders are denoted within square b

49、oxes in block diagrams. In Fig.1.3 this is demonstrated for vector expanders. The LTI systems operating on vector signals are called multiple i</p><p>　　1.1.2 Some multirate definitions and identities</p&

50、gt;<p>　　The vector signals are sometimes obtained from the corresponding scalar signals by blocking. Conversely, the scalar signals can be recovered from the vector signals by unblocking. The blocking/unblocking

51、operations can be defined using the delay or the advance chains [61], thus leading to two similar definitions. One way of defining these operations is shown in Fig.1.4, while the other is obtained trivially by switching