一种新的PLC系统窄带干扰抑制算法

针对电力线通信中的窄带干扰( NBI,Narrowband Interference),提出了一种基于循环前缀的递推最小二乘(CP-RLS,Cyclic Prefix-recursive Least Square)算法,并将该算法与干扰抑制-正交频分复用(IS-OFDM,Interference Suppressing-orthogonal Frequency Division Multiplexing)技术、循环前缀-分块最小均方算法(CP-BLMS,Cyclic Prefix-block Least Mean Square)以及线性快速分块最小均方(LFBLMS,Linear Fast Block Least Mean Square)算法进行了比较.仿真结果表明,在NBI功率较大时,CP-RLS算法的BER性能与CP-BLMS、LFBLMS算法相比提高了一个数量级,且随着NBI功率进一步增加,其BER性能几乎没有变差;CP-RLS算法更适合信道条件复杂多变的电力线通信.     

多用户下行链路收发机鲁棒性优化算法

该文提出了一种多用户MIMO系统中预矩阵及其匹配接收矩阵的鲁棒性优化算法。针对信道估计误差构造均方误差代价函数,在发射功率受限的约束条件下联合设计发射和接收矩阵,推导出了采用线性多用户MMSE接收时的最优收发机迭代算法。仿真表明,系统具有良好的鲁棒性。     

α噪声环境下整体最小平均P-范数IIR自适应滤波算法

当无限冲激响应(IIR)系统输入和输出信号中都存在α稳定噪声干扰,传统的最小平均P-范数算法(LMP)的解会出现较大偏差,本文提出了一种自适应IIR滤波整体最小平均P-范数(IIR_TLMP)算法,算法中整体考虑输入和输出信号的α稳定噪声干扰,将最小化l_p范数Rayleigh商采用随机梯度法得到自适应IIR滤波方程。通过仿真首先考察了特征指数和步长因子等主要参数对TLMP算法性能的影响,最后分别在时不变和时变系统中,将TLMP算法与LMP算法的性能在进行了比较,结果显示TLMP有更快的收敛速度和更小的误差。      

基于批处理传输方案的移动流媒体系统的缓存分配算法

该文介绍了在WCDMA网络中基于代理的移动流媒体系统以及评价其中代理服务器缓存分配算法性能的平均网络传输成本和移动终端的平均播放启动延时这两个指标;推导出在移动批处理(MBatching)传输方案下与这些指标相对应的节省值和综合节省值的计算公式;提出了适用于移动流媒体系统的,使所有流媒体节目的总的综合节省值最大的缓存分配算法。仿真结果表明,该算法与其他分配算法相比,可使总的综合节省值更大,节省更多的网络传输成本,取得更大的字节命中率。     

Stochastic analysis of the least mean fourth algorithm for non-stationary white Gaussian inputs

This paper studies the stochastic behavior of the least mean fourth (LMF) algorithm for a system identification framework when the input signal is a non-stationary white Gaussian process. The unknown system is modeled by the standard random-walk model. A theory is developed which is based upon the instantaneous average power and the instantaneous average squared power in the adaptive filter taps. A recursion is derived for the instantaneous mean square deviation of the LMF algorithm. This recursion yields interesting results about the transient and steady-state behaviors of the algorithm with time-varying input power. The theory is supported by Monte Carlo simulations for sinusoidal input power variations.     

Convergence analysis of a continuous-time nonlinear LMS-type algorithm using stochastic calculus

In several applications least mean square (LMS) has served as a good tool for estimating the parameters of linear models but the success of continuous-time in nonlinear models has not reached its height. In this paper, we have developed a nonlinear continuous-time LMS type algorithm that estimates parameters of nonlinear systems considering the noisy input–output relationship. The nonlinear system has been assumed to be memoryless and an additive Gaussian noise component to the system has been assumed. The mean squared error between the true system output and the estimated output, when the estimated output is modeled using the same form of the nonlinear function as the original system but with the parameters unknown, is minimized using the gradient scheme with the expectation removed. The result is a least mean square algorithm for nonlinear systems. In particular, we have performed a convergence analysis of the continuous-time nonlinear LMS algorithm applied to nonlinear systems when the time step goes to zero. The resulting algorithm then behaves as a stochastic differential equation, and the standard methods of Itô calculus and Fokker–Planck theory are applied to obtain statistical properties of the mean and covariance evolution of the parameter estimates. Computer simulations corroborate the theoretical results.     

零极值目标函数系统辨识算法

该文提出一种零极值目标函数最小化系统辨识算法。目标函数为系统均方误差与系统噪声方差之差的平方,其极小值为零。在系统辨识过程中采用滑动平均法在线估计系统均方误差、输入自相关矩阵以及输入与期望响应的互相关向量。推导出自适应滤波器权值向量的更新表达式。算法的步长能够根据统计量自适应地调整,使得在得到较小稳态误差的同时提高算法收敛速度。分析了算法的稳定性,得到了算法收敛的条件。对比实验结果表明,该算法具有更快的收敛速度,更小的稳态误差以及更好的稳定性。     

一种多天线UWB系统中线性预编码算法

线性预编码提高了多天线UWB系统的传输速率和误码率性能。通过选择合适的参数,系统最大限度地达到最大吞吐量,而多天线技术提高了系统空间分集增益。针对传统的ZF算法限制误码率性能提高的问题,提出了一种新的预编码方案,实现简单。仿真结果表明,新算法进一步减小系统误码率,提高了系统性能,较传统的ZF算法性能更为优越。     

基于AHMT放大转发协作网络中继选择算法*1*

对于单源多中继放大转发(Amplify and forward, AF)协作网络,传统中继选择方法存在难以在性能和复杂度之间取得折中的问题。本文结合最佳调和平均和SR门限算法,提出了一种基于AHMT(_SApproximate Harmonic Mean with Threshold, AHMT)中继选择算法。该算法首先在中继节点处设置信噪比门限值,然后将源S到中继R链路的瞬时信噪比大于门限值的中继作为待选中继。最后在待选中继集合中通过最佳部分信道AHMT算法选出最佳中继。仿真结果表明：与传统算法相比,所提算法不仅降低了系统的复杂度,而且性能更优,在高信噪比和中继个数较多时均可逼近传统最佳调和平均算法。     

Redundancy allocation to maximize a lower percentile of the systemtime-to-failure distribution

An algorithm is presented which solves the redundancy-allocation problem when the objective is to maximize a lower percentile of the system time-to-failure distribution. The algorithm uses a genetic algorithm to search the prospective solution-space and a bisection search as a function evaluator. Previously, the problem has most often been formulated to maximize system reliability. For many engineering-design problems, this new formulation is more appropriate because there is often no clearly defined mission time on which to base component and system reliability. Additionally, most system designers and users are risk-averse, and maximization of a lower percentile of the system time-to-failure distribution is a more conservative (less risky) strategy compared to maximization of the mean or median time-to-failure. Results from over 60 examples clearly indicate that the preferred system design is sensitive to the user's perceived risk. We infer from these results that engineering-design decisions need to consider risk explicitly, and use of mean time-to-failure as a singular measure of product integrity is insufficient. Similarly, the use of system reliability as the principal performance measure is unwise unless mission time is clearly defined     

Stochastic convergence analysis of recursive successive over-relaxation algorithm in adaptive filtering

A stochastic convergence analysis of the parameter vector estimation obtained by the recursive successive over-relaxation (RSOR) algorithm is performed in mean sense and mean-square sense. Also, excess of mean-square error and misadjustment analysis of the RSOR algorithm is presented. These results are verified by ensemble-averaged computer simulations. Furthermore, the performance of the RSOR algorithm is examined using a system identification example and compared with other widely used adaptive algorithms. Computer simulations show that the RSOR algorithm has better convergence rate than the widely used gradient-based algorithms and gives comparable results obtained by the recursive least-squares RLS algorithm.     

平方根变步长lp范数LMS算法的稀疏系统辨识

针对稀疏未知系统的辨识问题,提出了一种基于lp(0相似文献   

基于MMSE算法的MIMO-OFDM系统频率同步

MIMO-OFDM技术将成为4G的核心技术,但其频率同步问题是必须要解决的问题之一。传统的盲同步算法中,大多在精确度和复杂度之间很难取舍。针对LS(最小二乘)算法精确度不高的问题,提出了一种新的基于MMSE(最小均方误差)算法的频率同步方法,并与LS算法的性能进行了比较。仿真结果表明,在同样的信噪比下,MMSE算法能得到比LS算法更低的误码率和均方误差。     

SC-FDE系统中归一化FBLMS算法的研究

单载波频域均衡（SC-FDE）系统传统上采用最小均方误差（MMSE）算法和快速块最小均方（FBLMS）算法进行频域均衡,文中对单载波频域均衡系统中归一化FBLMS算法进行了仿真研究,并与MMSE算法和FBLMS算法进行比较。测试和分析了系统的均方误差和误码率。仿真结果表明：该算法克服了收敛速度和失调量之间的矛盾,提高了算法的收敛速度,降低了误码率,该结果对SC-FDE系统的进一步研究具有参考价值。     

Mitigation of Multipath Effects Based on a Robust Fractional Order Bidirectional Least Mean Square (FOBLMS) Beamforming Algorithm for GPS Receivers

Consideration on positioning and location services among the public has been increasing in the recent years with their applications in most of the anticipating milieus such as automobile navigation system etc. This insists for a development of high recitation global navigation satellite system such as global positioning system (GPS). Multipath effects, interference, signal jamming etc. are the major sources of error influencing the performance of the GPS receiver. Literature presents many of the multipath mitigation techniques. Among them, adaptive processing technology based beamforming algorithms appears a viable solution for multipath mitigation. The least mean square (LMS) beamforming algorithms were sensitive to dynamic environments thus affecting the accuracy of GPS. In this paper, an adaptive beamforming algorithm called fractional order bidirectional least mean square (FOBLMS) algorithm is proposed to mitigate the multipath effects and to conceal the jammer signal in a GPS receiver. The FOBLMS is an integration of the fractional calculus and bidirectional least mean square algorithm. The effectiveness of the proposed algorithm is validated using the bit error rate and experimentation gain results over the existing beamforming algorithms. Experimental results demonstrated that the performance of the proposed beamforming algorithm is better than LMS algorithm with maximal relative antenna gain of 28.92 dB, 32.84 dB for two and four element antenna arrays at ??60° and 10°, direction of arrivals respectively. The outcome of this work would be useful for developing a robust technique for multipath mitigation in GPS receivers.

     

Two-dimensional frequency-domain blind system identification usinghigher order statistics with application to texture synthesis

In this paper, Shalvi and Weinstein's (1993) super-exponential (SE) algorithm using higher order statistics for blind deconvolution of one-dimensional (1-D) linear time-invariant systems is extended to a two-dimensional (2-D) SE algorithm. Then, a 2-D frequency-domain blind system identification (BSI) algorithm for 2-D linear shift-invariant (LSI) systems using the computationally efficient 2-D SE algorithm and the 2-D linear prediction error filter is proposed. In addition to the LSI system estimate, the proposed BSI algorithm also provides a minimum mean square error (MMSE) equalizer estimate and an MMSE signal enhancement filter estimate. Then, a texture synthesis method (TSM) using the proposed BSI algorithm is presented. Some simulation results to support the efficacy of the proposed BSI algorithm and some experimental results to support the efficacy of the proposed TSM are presented. Finally, some conclusions are drawn     

一种新型自适应最小均方算法

针对最小均方(LMS)算法应用于功率放大器时存在收敛速度与收敛精度相矛盾的问题,提出了基于步长比较的最小均方算法。在带有P因子的变步长最小均方算法的基础上,采用简化的Sigmoid函数对步长进行改进,通过对前后两次步长的比较来确定是否更新权系数,以误差的自相关时间均值及均方误差的时间均值来调节算法步长,可以加快算法的收敛速度,降低算法的计算量。仿真结果表明,与最小均方算法相比,经过自适应预失真处理后,该算法的误差向量幅度(EVM)值提高了2.653 2%,系统邻信道功率比(ACPR)减少了4 dB。     

A new hybrid adaptive combination technique for ECG signal enhancement

This paper proposes a new de-noising system technique which is composed of Adaptive line enhancer (ALE) with the discrete wavelet transform (DWT) in order to improve the demerit of the ALE. A new adaptive algorithm which depends mainly on the second order resemblance between a signal and its delayed version is also derived and proposed for the ALE. Unlike the conventional DWT process where an estimation of a specific threshold is taken into account, here the ALE based proposed adaptive algorithm is exploited to enhance the detail coefficients. Therefore, the entire system works well for canceling Gaussian and non-Gaussian noise. Some experiments are carried out on an ECG signal to show the effectiveness of the proposed system. It illustrates from the simulations that the proposed technique demonstrates spectacular results for separating various noise types from the contaminated ECG signal. Finally, the proposed adaptive algorithm is compared with the leaky least mean square algorithm of the bases of mean square error. It is found that the performance of the proposed algorithm provides faster convergence rate and lower steady-state error. Consequently, the overall proposed system model represents a workable solution for ECG signal enhancement.

     

Failure time analysis for LMS algorithms

Few results are available in the literature on failure time (in contrast with long time average) analysis of the least mean square (LMS) adaptive algorithms (filters). Such analysis is extremely important when failure of the algorithm could cause failure of the entire system in which they are employed. A system may fail upon the first occurrence of a large error (any value far from 0), or upon staying far from 0 for a period of time (a clump of large errors). We use a Poisson approximation to study excursions (failure) of the LMS algorithm and its three signed variants. The distribution and the mean of the first excursion are approximated with the mean and distribution of an exponential distribution. The number of excursions in n units of time is approximated by a Poisson distribution. We also approximate the mean of an excursion length (clump size). The approximations are derived asymptotically as the excursion-defining set converges to the empty set, and as the algorithm step size converges to 0     

FH-OFDM系统同步算法研究与FPGA实现

研究了FH-OFDM系统的跳频同步方法和定时同步方法。跳频同步中利用GPS同步脉冲保证了接收机的时间基准。定时同步中使用了基于最小均方差的判决方法,对匹配滤波器进行了改进,有效地减少了载波频偏的干扰,并针对FPGA应用对算法进行了简化。对FH-OFDM同步系统进行了FPGA实现,性能测试结果表明该同步实现方法可以快速准确地检测到信号帧以及各个符号的起始位置。