一种适用于高阶QAM系统Bussgang类盲均衡新方法

该文提出一种适用于高阶QAM系统的新Bussgang类多模盲均衡算法。将新的代价函数与星座匹配误差函数结合,给出一种瞬时双模切换混合算法,然后设计了一种基于判决圆的模式切换准则,从而达到进一步有效降低新算法稳态误差的效果。最后,仿真结果以高阶256QAM信号为例,验证了该算法对于高阶QAM信号的盲均衡能力和良好效果。     

适用于圆形高阶QAM信号的盲均衡算法

针对现有盲均衡算法大多仅适用于方形或十字形QAM信号的问题,本文提出了一种适用于圆形高阶QAM信号的混合盲均衡算法。该算法首先对区域多模盲均衡算法进行改进,提出了新的区域划分方法并将其与改进的多模盲均衡算法相结合,构造了新的误差模型使之与圆形星座匹配程度大大提高;然后将其与判决引导算法和数据重用技术有机结合,构成低稳态误差的快速混合盲均衡算法。与其他同类型盲均衡算法相比,本文算法对于圆形星座高阶QAM信号尤为有效,不仅降低了稳态误差,且大幅度减少了收敛所需符号数。仿真结果验证了该算法的有效性。      

Improved Weighed Multi -modulus Blind Equalization Algorithm

数字有线电视主要采用QAM调制形式.针对QAM调制信号的特点,在加权多模盲均衡算法的基础上,引入Sigmoid 函数的变形,提出了一种加权值随均方误差自适应变化的新算法.新算法不需要设定模式转变的门限值,使算法模式从MCMA随均衡输出自适应转化为DD模式.通过仿真表明该算法与多模盲均衡算法(WMMA)相比降低了仿真复杂度,具有更稳定的收敛性能,适应于高阶QAM信号.     

一种改进的加权多模盲均衡算法

数字有线电视主要采用QAM调制形式。针对QAM调制信号的特点,在加权多模盲均衡算法的基础上,引入Sigmoid函数的变形,提出了一种加权值随均方误差自适应变化的新算法。新算法不需要设定模式转变的门限值,使算法模式从MCMA随均衡输出自适应转化为DD模式。通过仿真表明该算法与多模盲均衡算法(WMMA)相比降低了仿真复杂度,具有更稳定的收敛性能,适应于高阶QAM信号。     

适用于高阶QAM信号的分数间隔混合盲均衡算法

为了提高高阶QAM信号的盲均衡性能,提出一种分数间隔混合盲均衡算法。该算法将恒模盲均衡算法(CMA)与判决引导算法相结合,并充分利用了分数间隔均衡结构在收敛速度、稳态误差方面的优势。均衡器系数更新首先进行时域解相关,为了消除恒模盲均衡算法的相位不敏感特性,在实现上构造载波同步与盲均衡的大环路。仿真结果表明,对方形星座图的高阶QAM信号,该算法可以提供更快的收敛速度和更小的稳态误差。     

利用无约束函数的QAM信号自适应盲均衡方法

本文针对正交幅度调制(QAM)信号的盲均衡与载波相位恢复问题,首先在对现有的基于功率最大化盲均衡算法分析的基础上,依据向量范数间的关系,给出了一种QAM信号盲均衡与载波相位恢复的无约束代价函数;然后对代价函数的收敛性和解的唯一性进行严格论证,证明代价函数在取得最优解时能够同时实现盲均衡与载波相位恢复;之后将其转化为采用随机梯度法实现的自适应算法。相对CMA、MMA等其他自适应算法,新算法可获得更好的性能,通过对高阶QAM信号的均衡仿真,进一步验证了算法的有效性。      

基于回声状态网络的卫星信道在线盲均衡算法

针对非线性卫星信道,该文提出了两种基于回声状态网络(ESN)的在线盲均衡算法。利用ESN良好的非线性逼近能力,将发送信号的高阶统计量(HOS)代入ESN,结合常模算法(CMA)和多模算法(MMA)构造盲均衡的代价函数,并采用递归最小二乘(RLS)算法对ESN输出权值进行迭代寻优,实现了Volterra卫星信道下常模和多模信号的在线盲均衡。实验表明,该文算法可以有效降低非线性信道对发送信号产生的畸变,相较于传统的Volterra滤波方法,有更快的收敛速度和更低的均方误差值。     

基于隐马尔科夫模型的CPM信号盲Turbo均衡算法

针对高阶连续相位调制信号(CPM)盲均衡中存在的均衡性能较差以及不容易收敛等问题,从隐马尔科夫模型(HMM)的角度出发,建立一种新的EM-SOVA信道盲均衡方法,并结合Turbo均衡思想,提出了一种适用于高阶CPM信号的盲Turbo均衡算法。该算法通过将SOVA内嵌到EM算法的迭代过程中,有效改善了信道均衡效果,同时使用Turbo均衡的软信息迭代处理来进一步提高低信噪比条件下系统性能。理论分析和仿真结果表明,所提算法具有良好的盲均衡性能以及收敛性。     

基于FABA-SISO的时变频率选择性衰落信道CPM信号盲均衡

该文针对时变频率选择性衰落信道下高阶连续相位调制(CPM)信号盲均衡中存在的均衡性能较差、复杂度较高以及收敛速度慢等问题,从双向自适应信道均衡的角度出发,将线性调制信号均衡中使用的前后向自适应软输入软输出(FABA-SISO)算法推广,建立一种新的基于FABA-SISO的信道盲均衡方法,并结合逐幸存处理(PSP)思想和Kalman滤波,提出一种适用于高阶CPM信号的自适应盲均衡算法。该算法通过使用FABA-SISO算法,同时利用过去、现在和将来的观察数据进行Kalman滤波信道估计,有效改善了信道估计的精度,同时使用PSP算法来降低系统的复杂度,使得算法具有较好的工程应用性。仿真结果表明所提算法具有良好的盲均衡性能以及收敛性。     

适合高阶QAM信号的加权多模盲均衡算法

该文提出了一种加权多模盲均衡算法。该算法结合了多模盲均衡算法和判决引导算法的各自优势,利用由判决符号的指数幂构成的加权项调整代价函数中的模值。在均衡器系数迭代过程中,加权项不仅随着判决符号自适应地改变,还可以根据MSE估计值作更精确地调整。理论分析和仿真结果表明,与多模盲均衡算法等其它算法相比,该文提出的算法在同等条件下可以获得更快的收敛速度和更低的稳态收敛残差,更适用于高阶QAM信号。     

Modified higher order spectral analysis based TDE algorithm for CFAR signal detection problem

With the conditions of small data size and low Signal-to-Noise Ratio (SNR), the application of Higher Order Statistics (HOS) is restrained not only by its high estimation variance, but also by its low estimation precision. Therefore, a modified HOS based Time Delay Estimation (TDE) algorithm is proposed to overcome these problems. Comparing with the conventional TDE algorithms, the estimation variance is improved greatly. A typical simulation example is completed in order to test the performance of the algorithm proposed, which shows that the proposed algorithm has advantages over the traditional ones in both detection performance and computation efficiency.     

HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels

In this paper, we concentrate on the direct semi-blind spatial equalizer design for MIMO systems with Rayleigh fading channels. Our aim is to develop an algorithm which can outperform the classical training-based method with the same training information used and avoid the problems of low convergence speed and local minima due to pure blind methods. A general semi-blind cost function is first constructed which incorporates both the training information from the known data and some kind of higher order statistics (HOS) from the unknown sequence. Then, based on the developed cost function, we propose two semi-blind iterative and adaptive algorithms to find the desired spatial equalizer. To further improve the performance and convergence speed of the proposed adaptive method, we propose a technique to find the optimal choice of step size. Simulation results demonstrate the performance of the proposed algorithms and comparable schemes.     

基于多条件约束的ASON动态组播路由算法

在将自动交换光网络(ASON)建模成着色多重图的基础上采用综合成本策略,设计了一种新的最小综合成本路径计算算法,并结合组播成员的动态变化特性提出一种满足多条件约束的动态组播路由(DMRMC)算法,使组播路由和波长分配在同一过程内完成,且尽可能使组播树的综合总成本最小,同时局部优化波长转换次数、分光次数和不同波长的使用数量。仿真实验表明,本文算法有效可行,与采用单成本策略的组播算法相比,其取得了更低的综合成本和更好的连接阻塞性能。     

Blind adaptive XPIC based on HOS

This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the performance of the proposed adaptive blind algorithm, compared with the conventional algorithms, is outstanding with the feature of feasibility, stability and fast convergence rate.     

An optimized forwarding protocol for lifetime extension of wireless sensor networks

Optimized routing (from source to sink) in wireless sensor networks (WSN) constitutes one of the key design issues in prolonging the lifetime of battery‐limited sensor nodes. In this paper, we explore this optimization problem by considering different cost functions such as distance, remaining battery power, and link usage in selecting the next hop node among multiple candidates. Optimized selection is carried out through fuzzy inference system (FIS). Two differing algorithms are presented, namely optimized forwarding by fuzzy inference systems (OFFIS), and two‐layer OFFIS (2L‐OFFIS), that have been developed for flat and hierarchical networks, respectively. The proposed algorithms are compared with popular routing protocols that are considered as the closest counterparts such as minimum transmit energy (MTE) and low energy adaptive clustering hierarchy (LEACH). Simulation results demonstrate the superiority of the proposed algorithms in extending the WSN lifetime. Copyright © 2008 John Wiley & Sons, Ltd.     

A Blind Adaptive QR-Based Lattice Multi-Channel Filter for Cellular Base-Station TDMA Transceivers with Antenna Arrays

A new adaptive algorithm for blind interference rejection and multipath mitigation is studied and applied to antenna array processing in TDMA cellular communication systems. It is shown how the estimation of multiple signals from different sources by means of a multi-sensor receiver can be formulated as a multi-channel deconvolution problem. The proposed method is based on High-Order Statistics (HOS) processing of the baseband vector samples at the antenna array output. The similarity between the cumulant-based solution and the standard multi-variable Least Squares solution is exploited to derive an efficient adaptive algorithm based on the vector lattice architecture. The algorithm is numerically stable, considerably less complex than other existing multi-channel methods using HOS processing and exhibits rapid convergence with respect to blind array processing algorithms using simple gradient-based minimization procedures.     

A novel test optimizing algorithm for sequential fault diagnosis

Once all available measurements are determined, the highest testability index of a complex system is determined. To achieve such highest index with the lowest test cost, AND/OR graph search algorithms were developed for years to determine an optimal or near-optimal test sequence. However, in most cases, achieving the highest testability index induces extremely high test cost. The purpose of this paper is to optimize test set and test sequence so as to cut down the test cost while keeping the required, not necessarily the highest, FIR (Fault Isolation Rate) satisfied. Traditionally, this is an NP-Complete problem, which makes the computation of optimal test set impractical for even the moderate-sized model. In this paper, a greedy method is proposed to get the optimized test set. Then, we combine the greedy method with discrete binary particle swarm optimization (DPSO) to construct a test sequential tree. With the specified FIR requirement satisfied, the lowest test cost is achieved. The proposed algorithms are illustrated and tested in a range of real-world systems. The effectiveness and accurateness of the proposed algorithm is verified by computational results.     

Independent component analysis for multiple-input multiple-output wireless communication systems

Independent component analysis (ICA), an efficient higher order statistics (HOS) based blind source separation technique, has been successfully applied in various fields. In this paper, we provide an overview of the applications of ICA in multiple-input multiple-output (MIMO) wireless communication systems, and introduce some of the important issues surrounding them. First, we present an ICA based blind equalization scheme for MIMO orthogonal frequency division multiplexing (OFDM) systems, with linear precoding for ambiguity elimination. Second, we discuss three peak-to-average power ratio (PAPR) reduction schemes, which do not introduce any spectral overhead. Third, we investigate the application of ICA to blind compensation for inphase/quadrature (I/Q) imbalance in MIMO OFDM systems. Finally, we present an ICA based semi-blind layer space-frequency equalization (LSFE) structure for single-carrier (SC) MIMO systems. Simulation results show that the ICA based equalization approach provides a much better performance than the subspace method, with significant PAPR reduction. The ICA based I/Q compensation approach outperforms not only the previous compensation methods, but also the case with perfect channel state information (CSI) and no I/Q imbalance, due to additional frequency diversity obtained. The ICA based semi-blind LSFE receiver outperforms its OFDM counterpart significantly with a training overhead of only 0.05%.     

基于嵌套张量模型的MIMO中继系统组合接收算法

目前在单向双跳多输入多输出(MIMO)中继系统中,基于嵌套张量模型的接收算法主要采用单步交替最小二乘(ALS)和KRF(Khatri-Rao Factorization)算法.在时变信道且实时性要求较高场景下,计算复杂度高是制约其应用的主要因素.为此,在对单向双跳MIMO中继系统建模基础上,提出了基于嵌套张量模型的双步组合接收算法.该算法通过对接收的数据张量进行重建,将符号估计和信道估计分离,充分利用ALS和KRF的算法优势,有效降低了计算复杂度.同时,对算法的可辨识性进行了分析.仿真结果表明,该算法保持了与传统嵌套PARAFAC的最小二乘(Nested PARAFAC ALS)算法的相同估计性能,在源天线个数变化时,计算复杂度降低了80%以上;在中继天线个数变化时,计算复杂度降低了50%以上.