一种新的最小均方误差线性合并算法

针对未编码的多输入多输出系统,将基于训练序列的最小均方误差(MMSE)信道估计算法与最优线性无偏估计结构(BLUE)相结合对已估计的信道参数进行估计.仿真结果表明,使用线性合并的MMSE算法比传统的MMSE算法具有较小的参数估计误差,比使用线性合并的LS算法性能更好.     

在脉冲噪声环境中用于快衰信道估计的改进型算法

该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法.在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS(Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两利算法对噪声干扰非常敏感.该文提出改进型的RLM算法和Kalman滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度.     

窄带波束信道MIMO系统的容量分析

最近的研究表明,多输入多输出(MIMO)技术在不增加功率和带宽消耗的情况下具有大幅提高无线通信速率的潜力.在传统的MIMO系统(称为天线信道MIMO系统)中,多个接收天线的输出被直接选作多输出信号.提出了波束信道MIMO系统的结构.在波束信道MIMO系统中,多个波束的输出被选作多输出信号.基于阵列方向响应矢量,提出了窄带MIMO信道冲激响应矩阵的仿真算法.基于提出的信道冲激响应矩阵算法,给出了天线信道MIMO系统和波束信道MIMO系统容量极限的分析算法.理论分析和仿真结果都表明:波束信道能够提高信噪比(SNR),降低信道间的互相关性,因此波束信道MIMO系统比天线信道MIMO系统具有更大的容量极限.     

基于快速贝叶斯匹配追踪的激光大气信道估计

信道估计是指接收机获知信道状态信息的方法和过程。信道估计的准确度决定了接收机的工作性能,所以均衡之前,必须先进行信道估计。目前,激光光学传输信道估计成为多输入多输出正交频分复用的自由空间光通信系统的关键技术。传统的压缩感知方法作为一种信道估计的有效方法,具有恢复和重构原始信号的能力,但在计算复杂度上付出了一定的代价。快速贝叶斯匹配追踪算法克服了现有方法重构精度低和复杂度高缺点。通过先验模型选择和近似的最小均方误差的参数向量的估计,快速贝叶斯匹配追踪算法提供了估计信道冲激响应的一种有效方式。仿真结果表明,与传统的基于压缩感知的方法相比,该方法能显著提高系统的性能。     

非直达波条件下的TDD高精度移动定位算法

本文基于单次散射双向移动信道模型及信道参数联合估计,利用单基站测得的多条散射路径的到达时延、到达方向和离波方向进行定位,提出了一种非直达波条件下的TDD高精度移动定位算法.随后分析了其定位误差的分布特性.仿真结果验证了非直达波条件下该算法的有效性.     

基于支持向量机的非线性信道盲均衡算法研究

针对非线性信道盲均衡问题,考察了一种基于支持向量机的单输入单输出(SISO)盲均衡算法,该算法利用通信信号的常数模特性,将非线性盲均衡问题转化为非线性支持向量回归问题。在此基础上,本文利用分集技术,将该算法拓展至单输入多输出(SIMO)的情况。对两种算法进行的计算机仿真表明,基于支持向量机的SISO盲均衡算法能够有效地抑制信道中的非线性码间干扰;本文提出新算法由于更好地利用了信道的时空特性,具有剩余平均模误差小的优点。     

基于时延分集的MIMO多径信道盲估计算法

发送时延分集在空时编码中是一种有效可行的方法。本文提出了一种基于发送时延分集的多径信道估计方法,指出如果发送时延大于信道时延,多输入多输出（MIMO）信道可以转化为特殊的单输入多输出信道,通过对信道输出进行子空间分解可以估计出信道参数。仿真结果显示了本文算法的有效性。     

编码理论、信道理论与技术、编译码器

本文介绍了基于信道输入确定模型的多信道 FIR系统的有效盲识别算法,通过多信道识别去耦合,所提方法能够单独进行信道估计而不必解决增大信道响应,应用线性预测技术来实现此算法.计算有效并适合于实时应用。通过计算机模拟论证了此算法的有效性。参15     

基于遗传算法的盲信道估计新算法

基于单输入多输出的信道模型，该文提出一种基于遗传算法的信道盲估计算法，该算法的特点是基于低阶统计量，计算速度快，并且把信道阶数作为染色体的一部分参与估计，从而能在信道阶数未知的条件下同时对信道创数和参数进行估计，仿真结果表明该算法的性能优于现有的非迭代算法。     

长码MIMO CDMA信道的一阶聚类盲估计

该文提出了一种长码多输入多输出CDMA系统的信道盲估计方法.使用解相关匹配滤波器作为接收机前端,其输出信号可以剖分为信道矢量张成的线性空间.使用聚类算法提取集合的中心,从而仅利用解相关器输出的一阶统计量便可很好地估计出信道参数.同时对K-均值聚类算法的初始值设定进行了改进以提高收敛速度.所提出的算法有较低的计算复杂度.仿真结果表明该算法的有效性以及对信道阶数过估计有很好的鲁棒性.     

Blind MMSE Equalization of FIR/IIR Channels Using Oversampling and Multichannel Linear Prediction

A linear‐prediction‐based blind equalization algorithm for single‐input single‐output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second‐order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single‐input multiple‐output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum‐mean‐square‐error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.     

Blind identification of IIR systems based on special SIMO model

Based on oversampling the system output, this paper presents a deterministic approach to blind identification of fast changing infinite-impulse-response (IIR) systems. The contributions of this paper are: 1) we prove that oversampling the output of a single-input-single-output (SISO) IIR system is equal to transforming the SISO IIR system into a single-input-multiple-output (SIMO) IIR model with all subsystems have the same autoregressive (AR) coefficients. Based on this model, a new identification algorithm is proposed, which can give the least-squares approach; 2) we show that in the SIMO model, the number of subsystems can be varied and will affect the identification performance. We also discuss how to choose a proper subsystem number to guarantee the best performance; 3) we deduce the sufficient and necessary conditions for the system to be identifiable associated with the proposed algorithm. Since the proposed approach only needs a small quantity of data samples, it can be used for fast changing IIR systems. Computer simulations give some illustrative examples.     

一种基于IIR信道输出过采样的SIMO模型

通过对IIR信道输出的过采(Oversampling)，该文提出一种新的单输入多输出(Single-Input-Multiple-Output，SIMO)模型。较现有模型大大简化，可作为IIR信道盲辨识研究的依据。该文同时分析了采样率和SIMO模型子信道数的关系，并用计算机仿真给出他们对辨识效果的影响，可作为选择采样率和子信道数的参考。     

Blind channel estimation for single‐input multiple‐output OFDM systems: zero padding based or cyclic prefix based?

Orthogonal frequency division multiplexing (OFDM) transmission equipped with multiple receive antennas constitutes a single‐input multiple‐output (SIMO) OFDM system. SIMO‐OFDM systems have been widely used in wireless communications. Compared to those approaches using training sequences, blind channel estimation methods for SIMO‐OFDM systems have the advantage of saving bandwidth and improving energy efficiency and system throughput. As far as blind channel identification is concerned, it is known that zero padding (ZP)‐based single‐input single‐output (SISO)‐OFDM systems have desirable features compared to conventional cyclic prefix (CP)‐based SISO‐OFDM systems. However, it is yet unknown whether ZP‐ or CP‐based SIMO‐OFDM systems are favourable for blind channel estimation. To investigate this problem, we first propose a short‐data effective method for blind channel estimation for ZP‐based SIMO‐OFDM systems. Then we analyse a number of issues surrounding blind channel estimation for ZP‐ and CP‐based SIMO‐OFDM systems. The issues brought up in the paper have not been discussed in the existing research. The significance of our investigation is that it provides a deep insight into blind channel estimation for ZP‐ and CP‐based SIMO‐OFDM systems. Copyright © 2011 John Wiley & Sons, Ltd.     

A Recursive Blind Adaptive Equalizer for IIR Channels with Common Zeros

This paper considers the problem of blind adaptive equalization of infinite impulse response (IIR) channels without requiring the channel diversity condition. That is, the subchannels in the fractionally sampled model can have common factors. We analyze the case of two parallel channels, and develop an equalizer based on IIR prediction of the received signal. The predictor parameters are estimated by using the recursive extended least squares (RELS) algorithm. It is proved that with probability one the adaptive equalizer is globally stable, the parameter estimates are consistent, and the prediction error converges toward a scalar multiple of the input symbol sequence.     

一种基于子空间分解的IIR信道盲辩识算法

本文证明了对IIR信道输出进行过采样 (采样率是输入码率的整数倍 )可以转化成单输入多输出的多信道模型 ,并且不同的信道有相同的AR系数 .基于这一特性本文提出一种基于子空间分解的信道参数盲辩识方法 ,即不同信道的MA系数可以由输出信号的噪声子空间唯一确定 ,而AR系数则可以通过求解YW方程得到     

A Single Input-Multiple Output Time Reversal UWB Communication System

Time reversal is a promising technique for the improvement of UWB communication systems. Intersymbol interference (ISI) limits the system performance in such wireless systems. This paper presents a general ISI analysis for time reversal UWB communication systems. The time reversal UWB system gives good performance for rates below the coherence bandwidth but at higher data rates the performance of the system is limited by intersymbol interference and bit error rate saturates even for high signal-to-noise ratio. To mitigate the ISI effects, a single input/multiple output (SIMO) time reversal UWB system is used and its performance is analyzed. It is shown that by using a SIMO TR transceiver, ISI reduces and the system capacity increases. Transmitted signal power at SIMO time reversal decreases, therefore in low data rate SISO performance is better than SIMO, But in high rate scenario, SIMO TR suppresses the ISI better than the SISO TR and its performance is better than SISO TR. It is possible to compensate the reduced power by using a receiver with more sensitivity.     

MIMO信道容量的研究与仿真

MIMO技术可以在不增加带宽和发射功率的情况下提高信道容量和频谱利用率。本文使用MATLAB建立SISO、SIMO、MISO、MIMO等无线信道,进行综合仿真并分析。通过仿真,我们得知,MIMO不但能有效地提高信道的容量,同时可以改善信道的平均信道容量和中断信道容量。