6 呂林真 文獻(xiàn)翻譯

1、單位代碼01學(xué)號 1103100011分類號TN.92密級文獻(xiàn)翻譯功率控制與多用戶檢測系統(tǒng)院（系）名稱信息工程學(xué)院專業(yè)名稱通信工程學(xué)生姓名呂林真指導(dǎo)教師唐海玲2014年3月28日中文譯文1.功率控制與多用戶檢測系統(tǒng)1.1什么是功率控制與多用戶檢測技術(shù)功率控制:為使小區(qū)內(nèi)所有移動臺到達(dá)基站時信號電平基本維持在相等水平、通信質(zhì) 量維持在一個可接收水平，對移動臺功率進(jìn)行的控制。功率控制分為前向與反向功率控 制，反向功率控制又分為開環(huán)功率控制和閉環(huán)功率控制，閉環(huán)功率控制細(xì)分為外環(huán)功率 控制和內(nèi)環(huán)功率控制。功率控制是CDMA系統(tǒng)一項(xiàng)關(guān)鍵技術(shù)。CDMA系統(tǒng)是干擾受限 的系統(tǒng)，移動臺發(fā)射功率對小區(qū)內(nèi)通話的其

2、他用戶而言就是干擾，所以要限制移動臺發(fā) 射功率，使系統(tǒng)總功率電平保持最小。多用戶檢測：多用戶檢測技術(shù)（MUD）是通過取消小區(qū)間干擾來改進(jìn)性能，增加系 統(tǒng)容量。實(shí)際容量的增加取決于算法的有效性、無線環(huán)境和系統(tǒng)負(fù)載。除了系統(tǒng)的改進(jìn)， 還可以有效的緩解遠(yuǎn)近效應(yīng)。1.2多用戶檢測技術(shù)分析多址干擾（MAI）和遠(yuǎn)近效應(yīng)是已知的降解性能，并限制在CDMA移動通信系統(tǒng) 的容量的兩個主要因素。有應(yīng)付論文兩個問題的兩個關(guān)鍵手段：功率控制和多用戶檢測。 本文的目標(biāo)是設(shè)計(jì)有效的功率控制算法，多用戶檢測算法，并結(jié)合功率控制和多用戶檢 測算法，從而抑制多址干擾和克服遠(yuǎn)近效應(yīng)，從而提高整個系統(tǒng)的性能和容量。多用戶 檢測系統(tǒng)

3、又分為線性多用戶檢測算法與非線性多用戶檢測。線性多用戶檢測包括:1解相關(guān)多用戶檢測算法,2最小均方誤差（MMSE）檢測算,3自 適應(yīng)多用戶檢測算法,4盲自適應(yīng)多用戶檢測算法。由于最優(yōu)多用戶檢測法的復(fù)雜度太 高，1989年以后的研究均側(cè)重于準(zhǔn)最優(yōu)多用戶檢測法。準(zhǔn)最優(yōu)多用戶檢測可分為線性及 非線性兩大類。所謂線性或非線性，即是判斷算法的輸出是否是輸入的線性變換。線性 多用戶檢測算法主要包括去相關(guān)法和最小均方估計(jì)法（MMSE）。去相關(guān)法及MMSE 法的復(fù)雜度均隨用戶數(shù)線性增長，其中去相關(guān)法不需估計(jì)各用戶的幅度，具有較好的抗 遠(yuǎn)近效應(yīng)能力，而MMSE法需估計(jì)各用戶的幅度，抗遠(yuǎn)近效應(yīng)能力不如去相關(guān)法，但

4、 去相關(guān)法對信道噪聲有放大作用，MMSE法則沒有。當(dāng)信噪比較大時，使用去相關(guān)法較 好；當(dāng)信噪比較小進(jìn)，易于使用MMSE法。去相關(guān)性及MMSE法均需對互相關(guān)矩陣求逆，當(dāng)用戶數(shù)很多時，使用去相關(guān)法及 MMSE法的復(fù)雜度還是太大。為此有人提出了矩陣求逆的多項(xiàng)式分解法，只取多項(xiàng)式的 前幾項(xiàng)代替整個逆陣，從而化簡求逆的復(fù)雜度。非線性多用戶檢測包括：1串行干擾消除多用戶檢測算法，2并行干擾消除多用戶檢 測算法,3迫零解相關(guān)多用戶檢測算法。由于線性多用戶檢測法復(fù)雜度高，收斂慢，從可 實(shí)現(xiàn)性角度考慮的研究方向主要集中于非線性多用戶檢測方法。非線性多用戶檢測方法 主要有多級型、判決反饋型、神經(jīng)網(wǎng)絡(luò)等幾種方法。多

5、級型多用戶檢測算法，根據(jù)每一 級各用戶的檢測形式不同，又可劃分很多形式。若每一級各用戶并行的采用匹配濾波器 或相關(guān)器檢測，這就是傳統(tǒng)的并行干擾對消算法。若每一級的每個用戶，根據(jù)信號強(qiáng)度 的大小，采用串行的匹配濾波或相關(guān)檢測的方法，這就是所謂的串行干擾對消算法。當(dāng) 然，每一級各用戶還均可以采用去相關(guān)檢測、MMSE等算法，這時的性能會更好一些， 但算法實(shí)現(xiàn)復(fù)雜度也更高一些。多級型多用戶檢測算法的每級算法結(jié)構(gòu)相似，因而多級 型的每一級的最后（除最后一級），還有一個各用戶信號的再生、還原過程，這也是多 級型方法的特點(diǎn)之一。判決反饋多用戶檢測算法，有與多級型算法類似的種類。從本質(zhì)上看，判決反饋多 用戶檢

6、測算法等價于多級型算法。從結(jié)構(gòu)上來看，判決反饋法將多級型方法采用循環(huán)的 方式一級來完成，通過對一級的多次循環(huán)，完成多級型相同的功能。從實(shí)現(xiàn)上來看，判 決反饋多用戶檢測算法比多級型算法需要更多的存儲空間。1.3現(xiàn)狀及發(fā)展方向現(xiàn)狀及其發(fā)展方向：現(xiàn)有的多用戶檢測算法在計(jì)算復(fù)雜度與處理時延問題上存在不 足，且算法中一些參數(shù)（頻率、幅度、定時、相位等）估計(jì)有誤時，會使得相關(guān)矩陣產(chǎn)生較大 偏差，導(dǎo)致整個系統(tǒng)性能急劇下降。另一方面，當(dāng)前的MUD算法只考慮了同小區(qū)內(nèi)的 干擾，而沒有考慮相鄰小區(qū)間的同頻率用戶干擾。因此，今后的算法要在計(jì)算復(fù)雜度、收 斂性以及系統(tǒng)的魯棒性等方面進(jìn)行綜合的考慮，力求找到切實(shí)可行的多

7、用戶檢測算法。針對以上多用戶檢測算法的一些不足，近幾年研究的熱點(diǎn)趨向于以下幾方面：1半盲與盲多用戶檢測的研究盲多用戶檢測技術(shù)因不需訓(xùn)練序列、效率高、復(fù)雜度低等優(yōu)點(diǎn)而成為當(dāng)前研究熱點(diǎn) 之一。前面已提到了一些盲多用戶檢測的算法，最大的不足是算法收斂速度慢，特別是在 多徑信道下。2多用戶檢測與空間處理相結(jié)合在寬帶CDMA系統(tǒng)中，同信道干擾和碼間干擾成為影響系統(tǒng)穩(wěn)定性的主要障礙。空 時聯(lián)合處理能有效地抑制同信道干擾和碼間干擾。因?yàn)榭臻g濾波能抑制不同目標(biāo)用戶入 射方向的多址接入干擾,且可以把不同用戶和路徑的信號集中起來，進(jìn)而可增強(qiáng)目標(biāo)信號, 故該技術(shù)可增加系統(tǒng)容量。3多用戶檢測與優(yōu)化算法相結(jié)合優(yōu)化問題的

8、數(shù)學(xué)意義是在不等式約束條件下，求目標(biāo)函數(shù)最小或最大的一組設(shè)計(jì)變 量值。由于優(yōu)化不要求迭代過程嚴(yán)格收斂于數(shù)學(xué)意義上的最優(yōu)解，而是與其相鄰的一個解, 這樣不僅可節(jié)省計(jì)算時間,又可得到滿意的優(yōu)化結(jié)果。近年來，神經(jīng)網(wǎng)絡(luò)優(yōu)化技術(shù)的提出為解決多用戶檢測問題開辟了新的途徑，由于多用 戶檢測可以歸結(jié)為組合優(yōu)化問題,而神經(jīng)網(wǎng)絡(luò)具有高度并行、高度相互聯(lián)結(jié)、較好的容錯 性以及較強(qiáng)的自適應(yīng)能力，適合于解決優(yōu)化問題。發(fā)展方向：隨后又提出了大量次最佳多用戶檢測器方案，主要分線性多用戶檢測器 和非線性多用戶檢測器兩大類。其中線性多用戶檢測器主要包括:傳統(tǒng)信號檢測器、解相 關(guān)多用戶檢測器(DECMUD)、最小均方誤差檢測器(

9、MMSEMUD)等；非線性多用戶檢測 器主要包括:多級多用戶檢測器、判決反饋檢測器、相減干擾抵消檢測器等。由于算法復(fù) 雜度低、性能優(yōu)良的多用戶檢測算法是研究的重點(diǎn)之一。當(dāng)CDMA系統(tǒng)的擴(kuò)頻序列較 長時(對應(yīng)于CDMA系統(tǒng)的用戶容量較大)，多用戶檢測算法的復(fù)雜度高，處理時延 也較大。但對于擴(kuò)頻序列很長的CDMA系統(tǒng)，如WCDMA cdma2000，擴(kuò)頻序列長度最 大可達(dá)256，器件水平來實(shí)現(xiàn)多用戶檢測算法，還有很大的難度。這也反映出了 TD-SCDMA的優(yōu)點(diǎn)。摘自：功率控制算法在CDMA系統(tǒng)的應(yīng)用附：英文原文Power control and Multi-user detection techn

10、iquesWhat is the power control and multi-user detection techniquesPower Control: To enable all mobile stations within the cell reaches the base station signal level will remain at an equal level, communication quality can be maintained at a level to receive, control of the mobile station power level

11、. Power control is divided into the front and reverse power control and the reverse power control is divided into open loop power control and closed loop power control, closed loop power control is divided into inner and outer loop power control power control. Power control is a key technology in CD

12、MA systems. CDMA systems are interference limited systems, the mobile station transmit power to other users within the district that is in terms of the interference call, so to limit the mobile station transmit power, bringing the total system power levels kept to a minimum.Multi-user detection: mul

13、ti-user detection techniques (MUD) through inter-cell interference cancellation to improve performance, increase system capacity. Actual capacity depends on the algorithm to increase the effectiveness of the wireless environment and system load. In addition to improving the system, but also can effe

14、ctively alleviate the distance effect.Power Control and Multi-User Detection Technology AnalysisMultiple access interference (MAI) and the distance effect is known to degrade performance and limited to two main factors CDMA mobile communication system capacity. There are two key means to cope with t

15、he paper two problems: the power control and multi-user detection. Goal of this paper is to design an effective power control algorithm, multi-user detection algorithm, combined with the power control and multi-user detection algorithm, thus inhibiting multiple access interference and overcome dista

16、nce effect, thereby improving overall system performance and capacity.Linear multi-user detection include: 1 decorrelation multiuser detection algorithm, two minimum mean square error (MMSE) detection operator, 3 adaptive multiuser detection algorithm, four blind adaptive multiuser detection algorit

17、hm. Due to the complexity of the optimal multi-user detection method is too high, in 1989 after studies have focused on the quasi-optimal multi-user detection method. Quasi-optimal multi-user detection can be divided into linear and nonlinear two categories. The so-called linear or non-linear, ie, t

18、he output of the algorithm is to determine whether the input is a linear transformation. Linear multi-user detection algorithm to include the relevant law and the minimum mean square estimation (MMSE). To the relevant laws and the complexity of the MMSE method are linear increase with the number of

19、users, which do not need to go to the relevant laws of the magnitude estimated for each user, with good near-far effect, while the MMSE method to estimate the magnitude of each user, anti-proximity effect ability is better to relevant law, but to the relevant law has to channel noise amplification,

20、MMSE law no. When SNR is large, the use of the relevant law to better; When SNR into smaller, easy-to-use MMSE method.Correlation and MMSE method to the required cross-correlation matrix inversion, when a lot of users, use the method and the MMSE method related to the complexity is still too big. To

21、 this end it was suggested that the matrix inverse polynomial decomposition method, just take a few instead of the whole before the polynomial inverse matrix, thereby simplifying the complexity of the inverse.Nonlinear multi-user detection include: 1 serial interference cancellation multiuser detect

22、ion algorithm, two parallel interference cancellation multi-user detection algorithm, 3 ZF solution related multi-user detection algorithm. Due to the high complexity of linear multi-user detection method, slow convergence can be realized from the research point of view focused on the non-linear mul

23、ti-user detection methods. Several major multiple-stage type methods, decision feedback, neural networks and other non-linear multi-user detection methods. Multi-level multi-user detection algorithm based on the detection of each user in the form of each level is different, can be divided in many fo

24、rms. If each user every level parallel correlator or matched filter detection, which is the conventional parallel interference cancellation algorithm. If each user at every level, according to the size of the signal strength, the use of matched filtering or serial correlation detection method, which

25、 is called the serial interference cancellation algorithm. Of course, each user can be used at every level are also related to the detection, MMSE algorithm, then the performance will be better, but the algorithm complexity is higher. Each level algorithm-based multi-user multi-level structure detec

26、tion algorithm similar to each final (except for the last level), there is a regeneration signal for each user, and therefore the reduction process of multi-stage type-level, which is a multi-stage type methods One of the features. Decision feedback multiuser detection algorithm, a multi-stage type

27、algorithm with similar species. In essence, the decision feedback multiuser detection algorithm is equivalent to the multi-stage type algorithm. From a structural point of view, the decision feedback method using a multi-stage type methods to complete the primary cycle way through multiple cycles of

28、 level, complete the multi-stage type the same function. From the realization point of view, the decision feedback multiuser detection algorithms require more storage space than the multi-stage type algorithm.Status and Development DirectionStatus and Development Direction: existing multi- user dete

29、ction algorithm deficiencies in the computational complexity and processing delay problem and algorithm parameters (frequency, amplitude , time , phase, etc. ) with the error estimate would make the correlation matrix produce large deviation , resulting in a sharp decline in the overall system perfo

30、rmance. On the other hand , the current MUD algorithm only considers the interference within the same cell , the same frequency without considering the inter- user interference in neighboring cells . Therefore, the future of the algorithm to be considered in a comprehensive computational complexity

31、, convergence and robustness of the system and other aspects , and strive to find practical multi-user detection algorithm.For some shortcomings over multi-user detection algorithm research hotspot in recent years tended to the following aspects :Multi -user detection a semi-blind and blindBlind Mul

32、tiuser Detection technology because without training sequence , high efficiency, low complexity and become one of the current research focus. As previously mentioned some of the blind multiuser detection algorithm , the biggest drawback is the slow convergence , especially in multipath channel .Mult

33、i- user detection with a combination of spatial processingIn wideband CDMA systems, co-channel interference and inter- symbol interference as a major obstacle to stability of the system . Joint space-time processing can effectively suppress co-channel interference and inter- symbol interference . Be

34、cause the spatial filter can suppress multiple access incident direction different target user access interference, and the user can type and path signals together , and thus can enhance the target signal , this technique can increase the system capacity .Multi- user detection and optimization algor

35、ithm combinesMathematical sense optimization problems under inequality constraints , the objective function of the minimum or maximum value of a set of design variables . Because of the iterative optimization process is not required to converge to the optimal solution strictly mathematical sense , b

36、ut an adjacent solution , which not only saves computation time can be optimized to give satisfactory results .In recent years, neural network optimization techniques proposed to solve the problem of multi-user detection has opened up new avenues , due to multi- user detection can be attributed to c

37、ombinatorial optimization problems, and neural networks are highly parallel , highly interconnected , better fault tolerance as well as more strong adaptive ability , suitable for solving optimization problems.Directions: subsequently made a number of sub-optimal multiuser detector program , the main points of linear and non-linear multi- user detector multiuser detector into two categories. Linear multi- user de