当前位置:文档之家› 专业英语论文翻译

专业英语论文翻译

专业英语班级:姓名:学号:英文出处:/viewdoc/download?doi=10.1.1.132.1671&rep=rep1&type=pdf Daniel N. Liu and Michael P. FitzDepartment of Electrical EngineeringUniversity of California Los Angeles, Los Angeles, CA, 90095译文:STBC-MTCM 译码算法复杂度在快速衰落信道的简化摘要:STBC-MTCM 方案在多收发天线系统中是一个非常著名的传输分集方案,实现高速率,全分集和高的编码增益。

在时间选择性快速衰落信道或频率选择性衰落信道中,现有的STBC-MTCM 译码器性能具有不能降低的误码底板。

本文阐述两种计算有效的译码算法来抵偿快速衰落信道:归零(ZF )检测器和线性最小均方误差检测器(LMMSE )。

所提出的译码算法在信道条件从准静态衰落到时间选择性快速衰落(TSFF )或频率选择性衰落(FSF)变化时都有表现出了很好的特性。

仿真结果表明我们提出的译码算法接近ML 算法的性能,同时具有较好的计算效率。

I.引言在过去的十年里Telatar ,Foschini 和Gans 在无线通信系统多收发天线的理论容量方面进行了大量的研究。

为了能够充分利用分集技术和接近多入多出系统的信道容量,多年来提出了各种空时码方案,如:分层空时编码结构(LST ),空时分组码(STBC ),空时网格码(STTC ),以及空时分组码与—多维网格编码调制级联的STBC-MTCM 。

STBC 如文献[10]指出,空时分组码(STBC )以非常低的编码复杂度就可以提供全分集,但是它不能获得编码增益;STTC 不仅提供了全分集而且还能获得编码增益,但它以较高的编码复杂度为代价。

因此,用级联外码方法来折衷STBC 和STTC 的缺点是很自然的事,如在MTCM 的内部级联STBC ,这就是所谓的STBC —MTCM 的设计理念(这就产生了STBC-MTCM )。

在文[12]中,Siwamogsatham 和Fitz 通过扩展内码正交的STBC 码本,进一步加强了空时分组码原始速率损耗的STBC-MTCM 的设计。

由于内部STBC 的正交性,在准静态(慢)衰落信道中可以采用复杂度低的ML 译码算法。

在所有已被提出的空时编码方案中,准静态分组衰落是广泛应用的前提假设。

虽然这样的假设在很多的情况下均适用,但是时间选择性快衰落或频率选择性信道确实存在。

例如,zhu 等人证明:在一个正常的高速公路上以h Km /110的速度行驶,可以导致归一化的多普勒效应扩展T f d 达到0.9%。

在这种情况下,信道从一个符号到另一个符号将会有特别显著变化。

因此,我们在STBC-MTCM 方案中取消了对准静态衰落信道假设的约束。

在快速衰落信道中,内部的STBC 的最佳译码复杂度激励着人们寻求低复杂度的译码算法。

实际上,最佳译码器ML 内部的STBC 的复杂度是与吞吐量幂指数增长的。

主要原因是由于主要原因是似然函数是联合函数,不能分解为单一的符号函数之和的形式。

为了解决复杂度的问题,我们提出了两种计算简单的次最优的检测:归零检测器ZF 和线性最小均方值检测器LMMSE 。

由于STBC-MTCM 扩展码本的内部结构,一旦其他信号的子集属于另一信号的子集,滤波器的系数便可以通过酉变换来构造。

本文所提出的译码算法在信道条件由准静态衰落到TSFF 或FSF 状态变化时都能很好的特性。

而且,仿真结果表明,所提出的译码算法在快速衰落信道中接近ML 的特性,同时计算效率也更有效。

论文剩余部分的结构如下:第二部分概述了系统模型并引入相关符号,第三部分详细介绍了STBC-MTCM 低复杂度的译码算法,第四部分给出了STBC-MTCM 在快速衰落信道不同空间效率的几个计算例子。

第五部分为论文的总结。

II.系统模型A.外部编码器我们考虑一个有t n 条传输天线和r n 条接收天线的空时多入多出(MIMO )无线通信体系。

这种传输方案在图1的上部给出了详细的介绍。

值得注意的是:级联体系外码的级联可以是任意的(如BBC,LDPC 编码)。

例如:Nm 的网格MTCM 编码的通常可作为一个外码,一个m t N N *的STBC 通常作为内码。

假设长度为0Nk 的b 矢量作为源信息输入到码率为1/0k k Roc =的MTCM 编码器中。

每k 段编码区间,编码一个0k 比特的数据字时会产生一个1k 比特的数据字,它被速率为Nm k Ric /1=的内部STBC 映射输出空时码字C k ∈)(H 具有以下形式: )]1()([)(-+⋅⋅⋅=Nm kNm x kNm x k X (1)此时⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡=)(...)()()(21t x t x t x t nt x (2) }1.,1,{-+⋅⋅⋅+∈m m m m N kN kN kN t 。

假设符号)(t x i 是与符号线性调制相对应的,并且是在时间t 第i 条天线传输的传输信号。

此外,我们假设符号的平均能量|)(|2t x i Ex E ≡且信号在复杂的星座图中被选择的概率相同。

总的谱效率是R 可以定义为mic oc N k R R R /0==比特/信道利用位(BPCU )。

图1 STBC-MTCM 体系假设在信道中传输的相邻两个符号发生变化,且从一个天线到另一个天线的衰落是互相独立的。

在接收端,假设完全同步,接收信号Nm nr C k Y *)(∈可以表示成 )]1()([)(-+⋅⋅⋅=Nm kNm y kNm y k Y (3)特殊地,我们认为一个线性模型在符号瞬时时间t 接收的向量为1*1)](,),([)(nr T n C t y t y t y r ∈⋅⋅⋅=可以通过传输向量)(t x 表示为:)()()()(t t t t y n x H += (4)其中nt nr C t *)(∈H 是一个复杂的信道矩阵,在接收端是完全已知的,向量1*)(nr C t ∈n 是一个独立复杂的零均值高斯噪声,每个实分量的方差为2/02N =δ。

专门指出,)(k H 可以完全描述为:)]()()([)(21t h t h t h t nt ⋅⋅⋅=H (5) 此时T nri i i i t h t h t h t h )]()(),([)(21⋅⋅⋅=,)(t h ji 则描述了在时间t 时从天线传输端i 到天线接收端,...2,1,=j j 复杂的信道系数。

我们定义信噪比0/SNR N E b =,bE 是每条接收天线上传输一位信息所需的能量。

B .外部译码器 外部译码器的结构在图1下半部分给出了描述。

由于我们采用了MTCM 的编码器,级联的STBC-MTCM 系统的译码过程可以分为两个阶段:并行转换过程和非并行转换过程(i.e. MTCM 译码)。

一旦并行转换过程结束,接下来的任务就由Viterbi 算法直接给出。

并行转换过程包括可能性计算和为每个非并行转换过程选择最可能的并行转换过程。

由于信道的时间选择性,文献[12]中简化的ML 检测方法对于并行转换过程不再是最理想的了。

因为信号的正交性被毁坏以及似然函数在每个编码或译码区间内变成了联合的符号信息。

真正的ML 译码器为每个给定的网格分支寻找所有的并行转换。

与文[12]中简化的ML 检测器不同,复杂的全开ML 译码器以吞吐量的指数倍增长和以||ρ为基数的并行转换。

假设在第k 段译码区间里,从输出状态u 到输入状态v 的分支度量uv T 计算为21||);;()()(||)(min ∑-+=∈→-=Nm kNm kNm t p uv v u p t x t H t y k T ρ (6) 其中),;(v u p t x →定义为在瞬时信号时间t ,第p 个从状态u 到状态v 的并行转换向量。

此外,最可能的并行转换),(k v u p ML→∧从状态u 到状态v 在译码区间k 里可以选为),(k v u p ML →∧=21||);;()()(||arg m in ∑-+=∈→-Nm kNm kNm t p v u p t x t H t y ρ (7)为了简洁的讨论,我们定义),(k v u p ML →∧为ML p ∧。

一旦计算出了所有的分支度量,Viterbi 的算法则可以用于寻找信道的最小累加度量。

英文:Abstract —STBC-MTCM scheme which achieves high rate, full diversity and large coding gains is an outstanding example of transmit diversity scheme for multiple-antenna system. In the case of time selective fast fading (TSFF) or frequency selective fading (FSF) channels, the performance of current existing STBC-MTCM decoder suffers from an irreducible error poor. In this paper, we present two computational efficient decoding algorithms: zero-forcing (ZF) detector and linear minimum mean square error (LMMSE) detector to combat the fast fading channels. The proposed decoding algorithms provide a robust performance across range of channel conditions from quasi-static (slow) fading to TSFF or FSF. Simulation results suggest that our proposed decoding algorithms have near Maximum-Likelihood (ML) performance while beingcomputational more efficient.I. INTRODUCTIONThe seminal work by Telstar [1], Foschini and Gains [2] on theoretical capacity of multiple transmit-receive antennas in a wireless communication system has spawned a great dealof research in the last decade. In order to fully capture the Benet of the available diversity and to approach the multiple input multiple-output (MIMO) channel capacity, many space-time coding schemes have been proposed over the years: layered space-time (LST) architecture [3]; space-time block codes (STBC) [4], [5]; space-time trellis codes (STTC) [6]–[8] and STBC-multiple trellis-coded modulation (STBC-MTCM) [9]. As pointed out in [10], STBC provides full diversity with very low encoder/decoder complexity; it does not provide any coding gain; while STTC provides full diversity as well as coding gain but at the cost of much higher decoder complexity. Thus, it seems natural to compromise the two extremes of STBC and STTC by concatenate an outer code such as MTCM [11] with an inner STBC. This resulted in the so-called STBC-MTCM designs. In [12], Siwamogsatham and Fitz further enhance the original rate-flossy STBC-MTCM designs by expanding the codebook of the inner orthogonal STBC. Due to orthogonality of inner STBC, a complexity advantage for ML decoding is available for quasi-static (slow)fading channels.Among all these proposed space-time coding schemes [4]–[9], [12], quasi-static block fading has been a widely used assumption. While such an assumption can be met in fairly large number of scenarios, time selective (TS) fast fading [13], [14] or frequency selective (FS) [15] channels do exist. For example, Zhu et al. showed at a normal high way driving speed about 110 Km/h, which can induce normalized Doppler spread d up to 0.9% [13]. In such case, the channel may very significantly from symbol to symbol. Thus, we relax the constraint of quasi-static fading channel assumption in decoding STBC-MTCM schemes. The complexity of optimal ML decoder on the inner STBC in fast fading channels motivates the search for low complexity decoding algorithms. In fact, the optimal ML decoder on the inner STBC has complexity that grows exponentially with throughput. The main reason is that the likelihood function becomes joint and no longer able to decompose into the sum of individual symbol functions. To address the complexity issue, we derive two computational simple suboptimal detectors: zero-forcing (ZF) detectors and linear minimum mean square (LMMSE) detector. Due to the inherent structure of the expanded cookbook in STBC-MTCM, later coefficients can be constructed via unitary transformation for every other signal subsets once it is formed within one subset. The proposed decoding algorithms provide a robust performance across a range of channel conditions from quasi-static (slow) fading to TSFF or FSF. Furthermore, simulation results suggest that the proposed decoding algorithms has a near ML performance in fast fading channels, while being computational more efficient.fast fading channels, while being computational more efficient.The remainder of the paper is organized as follows: section II outlines the system model and introduces our notation. Section III details the reduced complexity decoding algorithms for STBC-MTCM. Section IV presents several numerical examples for different spatial efficiencies of STBC-MTCM in fast fading channels. Section V concludes the paper.II. SYSTEM MODELA. Outer EncoderWe consider a space-time multiple-input multiple-output (MIMO) wireless communication system with t transmit and receive antennas. The transmission scheme is detailed in the upper half ofFig. 1. Notice that the outer code for the concatenated system can be arbitrary (i.e. BCC, LDPC Code). For example: A MTCM encoder with trellis multiplicity of m is used as an outer code and a t × m STBC is used as an inner code. Let vector b with size 0 be source information bits entering the rate oc = 0 1 MTCM encoder. At the encoding interval, a 0-bit data word is encoded, yielding a 1-bit data word, which is then mapped by an inner STBC with rate ice = 1 m to output a space-time codewords.for ∈{ m m+1 ··· m+ m−1}.We denote that symbol corresponds to the symbol linear modulated and transmitted on the th antenna at time . Moreover, we assume the average symbol energy and symbols are equally likely chosen from a complex constellation. The overall spectral efficiency is then definedbits per channel use (BPCU). The channel is assumed to be varying from symbol to symbol of transmission and fades independently from antenna to antenna.At the receiver side, assuming perfect synchronization, the received signalNmnrCkY*)(。

相关主题