6 吕林真 文献翻译

1、单位代码01学号 1103100011分类号TN.92密级文献翻译功率控制与多用户检测系统院（系）名称信息工程学院专业名称通信工程学生姓名吕林真指导教师唐海玲2014年3月28日中文译文1.功率控制与多用户检测系统1.1什么是功率控制与多用户检测技术功率控制:为使小区内所有移动台到达基站时信号电平基本维持在相等水平、通信质 量维持在一个可接收水平，对移动台功率进行的控制。功率控制分为前向与反向功率控 制，反向功率控制又分为开环功率控制和闭环功率控制，闭环功率控制细分为外环功率 控制和内环功率控制。功率控制是CDMA系统一项关键技术。CDMA系统是干扰受限 的系统，移动台发射功率对小区内通话的其

2、他用户而言就是干扰，所以要限制移动台发 射功率，使系统总功率电平保持最小。多用户检测：多用户检测技术（MUD）是通过取消小区间干扰来改进性能，增加系 统容量。实际容量的增加取决于算法的有效性、无线环境和系统负载。除了系统的改进， 还可以有效的缓解远近效应。1.2多用户检测技术分析多址干扰（MAI）和远近效应是已知的降解性能，并限制在CDMA移动通信系统 的容量的两个主要因素。有应付论文两个问题的两个关键手段：功率控制和多用户检测。 本文的目标是设计有效的功率控制算法，多用户检测算法，并结合功率控制和多用户检 测算法，从而抑制多址干扰和克服远近效应，从而提高整个系统的性能和容量。多用户 检测系统

3、又分为线性多用户检测算法与非线性多用户检测。线性多用户检测包括:1解相关多用户检测算法,2最小均方误差（MMSE）检测算,3自 适应多用户检测算法,4盲自适应多用户检测算法。由于最优多用户检测法的复杂度太 高，1989年以后的研究均侧重于准最优多用户检测法。准最优多用户检测可分为线性及 非线性两大类。所谓线性或非线性，即是判断算法的输出是否是输入的线性变换。线性 多用户检测算法主要包括去相关法和最小均方估计法（MMSE）。去相关法及MMSE 法的复杂度均随用户数线性增长，其中去相关法不需估计各用户的幅度，具有较好的抗 远近效应能力，而MMSE法需估计各用户的幅度，抗远近效应能力不如去相关法，但

4、 去相关法对信道噪声有放大作用，MMSE法则没有。当信噪比较大时，使用去相关法较 好；当信噪比较小进，易于使用MMSE法。去相关性及MMSE法均需对互相关矩阵求逆，当用户数很多时，使用去相关法及 MMSE法的复杂度还是太大。为此有人提出了矩阵求逆的多项式分解法，只取多项式的 前几项代替整个逆阵，从而化简求逆的复杂度。非线性多用户检测包括：1串行干扰消除多用户检测算法，2并行干扰消除多用户检 测算法,3迫零解相关多用户检测算法。由于线性多用户检测法复杂度高，收敛慢，从可 实现性角度考虑的研究方向主要集中于非线性多用户检测方法。非线性多用户检测方法 主要有多级型、判决反馈型、神经网络等几种方法。多

5、级型多用户检测算法，根据每一 级各用户的检测形式不同，又可划分很多形式。若每一级各用户并行的采用匹配滤波器 或相关器检测，这就是传统的并行干扰对消算法。若每一级的每个用户，根据信号强度 的大小，采用串行的匹配滤波或相关检测的方法，这就是所谓的串行干扰对消算法。当 然，每一级各用户还均可以采用去相关检测、MMSE等算法，这时的性能会更好一些， 但算法实现复杂度也更高一些。多级型多用户检测算法的每级算法结构相似，因而多级 型的每一级的最后（除最后一级），还有一个各用户信号的再生、还原过程，这也是多 级型方法的特点之一。判决反馈多用户检测算法，有与多级型算法类似的种类。从本质上看，判决反馈多 用户检

6、测算法等价于多级型算法。从结构上来看，判决反馈法将多级型方法采用循环的 方式一级来完成，通过对一级的多次循环，完成多级型相同的功能。从实现上来看，判 决反馈多用户检测算法比多级型算法需要更多的存储空间。1.3现状及发展方向现状及其发展方向：现有的多用户检测算法在计算复杂度与处理时延问题上存在不 足，且算法中一些参数（频率、幅度、定时、相位等）估计有误时，会使得相关矩阵产生较大 偏差，导致整个系统性能急剧下降。另一方面，当前的MUD算法只考虑了同小区内的 干扰，而没有考虑相邻小区间的同频率用户干扰。因此，今后的算法要在计算复杂度、收 敛性以及系统的鲁棒性等方面进行综合的考虑，力求找到切实可行的多

7、用户检测算法。针对以上多用户检测算法的一些不足，近几年研究的热点趋向于以下几方面：1半盲与盲多用户检测的研究盲多用户检测技术因不需训练序列、效率高、复杂度低等优点而成为当前研究热点 之一。前面已提到了一些盲多用户检测的算法，最大的不足是算法收敛速度慢，特别是在 多径信道下。2多用户检测与空间处理相结合在宽带CDMA系统中，同信道干扰和码间干扰成为影响系统稳定性的主要障碍。空 时联合处理能有效地抑制同信道干扰和码间干扰。因为空间滤波能抑制不同目标用户入 射方向的多址接入干扰,且可以把不同用户和路径的信号集中起来，进而可增强目标信号, 故该技术可增加系统容量。3多用户检测与优化算法相结合优化问题的

8、数学意义是在不等式约束条件下，求目标函数最小或最大的一组设计变 量值。由于优化不要求迭代过程严格收敛于数学意义上的最优解，而是与其相邻的一个解, 这样不仅可节省计算时间,又可得到满意的优化结果。近年来，神经网络优化技术的提出为解决多用户检测问题开辟了新的途径，由于多用 户检测可以归结为组合优化问题,而神经网络具有高度并行、高度相互联结、较好的容错 性以及较强的自适应能力，适合于解决优化问题。发展方向：随后又提出了大量次最佳多用户检测器方案，主要分线性多用户检测器 和非线性多用户检测器两大类。其中线性多用户检测器主要包括:传统信号检测器、解相 关多用户检测器(DECMUD)、最小均方误差检测器(

9、MMSEMUD)等；非线性多用户检测 器主要包括:多级多用户检测器、判决反馈检测器、相减干扰抵消检测器等。由于算法复 杂度低、性能优良的多用户检测算法是研究的重点之一。当CDMA系统的扩频序列较 长时(对应于CDMA系统的用户容量较大)，多用户检测算法的复杂度高，处理时延 也较大。但对于扩频序列很长的CDMA系统，如WCDMA cdma2000，扩频序列长度最 大可达256，器件水平来实现多用户检测算法，还有很大的难度。这也反映出了 TD-SCDMA的优点。摘自：功率控制算法在CDMA系统的应用附：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