LTE-A下行信道估计算法研究

LTE-A（long term evolution advanced）通信系统采用正交频分复用（OFDM）为其物理层核心技术以获得抗频率选择性衰落和高频谱利用率等优点.接收端需要对多径信道动态估计和跟踪以满足相干解调,准确而简单的信道估计算法可以提高数据传输速率和降低用户设备的功耗.对LTE-A中基于导频的下行信道估计算法进行研究,提出了一种使用神经网络的信道估计算法.该算法考虑了下行传输链路的信道频率选择性,利用接收到的导频信号动态跟踪信道的变化特征,然后根据得到的信道信息对接收数据进行相干解调.仿真结果表明,与最小二乘（LS）、最小均方误差（MMSE）等传统方法相比较,该信道估计算法在均方误差（MSE）、误符号率（SER）和计算复杂度方面具有更好的性能,并且能保证LTE-A系统吞吐量需求.     

柔性双工网络功率分配:边剪枝加速的GNN计算

由于用户间干扰的存在,无线通信网络中的功率分配问题往往是非凸的、计算量巨大。当前图神经网络(graph neural network, GNN)成为一种有效的计算方法被用来解决该问题。为了最大限度地提高网络传输速率的同时降低计算复杂度,提出一种将设备属性和通信连接属性纳入GNN的柔性双工网络图表示方法,并构建了相应的柔性双工图神经网络(flexible duplex GNN, FD-GNN)模型,首次将节点对之间的距离、信道增益和邻居作为动态阈值引入到FD-GNN中,以适应动态环境。排除GNN中邻居的信道状态信息,通过修剪FD-GNN中的边来减少计算时间降低网络时间复杂度。仿真表明,所提出的基于信道增益邻居的阈值设定方法,性能最优且达到加权最小均方误差（weighted minimum mean square error,WMMSE）的97%,相较于Full-GNN所需的训练时间下降24%。提出的基于阈值的边剪枝有效降低了GNN运算的时间复杂度,提高了算法有效性。     

基于蜣螂算法优化的 DV-Hop 定位算法

针对无线传感器网络中传统DV-Hop(distance vector-hop)算法定位误差大的问题,提出了一种基于蜣螂算法优化的DV-Hop定位算法。首先使用双通信半径的方式细化节点间跳数,并使用最小均方误差准则计算锚节点的平均跳距,将改进后的平均跳距的平均值当做每个未知节点的平均跳距,最后引入权重因子优化适应度函数,使用蜣螂优化算法代替三边测量法进行坐标计算。仿真结果表明,所提算法比经典DV-Hop算法平均定位误差提升了55.69%、59.61%和67.59%,误差方差提升了52.41%、45.58%和36.87%,具有良好的定位精度和较好的稳定性。     

绝缘子污闪试验数据的稳健回归处理方法

提出了一种基于稳健同归的绝缘子污闪试验数据处理方法:根据试验电压与盐密和灰密数据,通过复加权最小二乘迭代算法求解回归系数;迭代计算中采用权重函数,权重系数为上次迭代的残差的函数,以此减少奇异值对回归系数的影响.通过比较试验数据与稳健回归及最小二乘回归的预测结果,验证了基于稳健回归方法在绝缘子污闪试验数据处理中的有效性.     

A distributed state estimator for electric power systems

The author develops a theoretically robust and computationally efficient distributed state estimator to solve the weighted least square state estimation problem by using distributed computation. This distributed state estimator is used in decentralized control and executes in a data communication network that is assumed to be topologically the same as and physically in parallel with the power network. Several attractive satellite functions can be obtained which include: (1) reduction of the time-skew problem; (2) freedom from the power network topological error; (3) easy identification of the unobservable states; and (4) bad data detection and identification. The computational complexity of this distributed state estimator was analyzed. This state estimator was simulated on several cases of the IEEE 30-bus system. The numerical accuracy of the simulation results is satisfactory, and the estimated computation time including the communication delay demonstrates the excellent computational performance of the distributed state estimator     

宽带电力线载波OFDM信道估计

正交频分复用（OFDM）是电力线高速数据传输的有效调制技术.而电力线信道的时频双选择性造成OFDM系统的频率偏移,导致载波间干扰（inter-carrier interference,ICI）.为准确掌握信道特性,提出了基于离散长球序列-基扩展模型（DPSS-BEM）的宽带电力线载波OFDM信道估计方法.采用二维DPSS-BEM对电力线信道进行建模,将电力线信道视为二维正交分量的线性加权,并利用导频位置的训练信息和最小二乘法估计权值.仿真表明,该方法能有效地跟踪电力线信道变化,改善信道的归一化均方误差和误码率性能,以便有针对性地采取措施,减少载波间的相互干扰.     

ICI自消除机制下的OFDM信道估计

在正交频分复用(OFDM)系统中,相干解调比差分解调有3 dB的增益,所以一般采用相干解调.为获得准确的信道信息,就需采用基于导频辅助(PSA)的信道估计方法.而在移动多径信道中,时变性致使子载波之间不再严格正交,从而造成子载波间干扰(ICI),影响信道估计性能.对此文中提出了1种基于ICI自消除的新型OFDM信道估计...     

Robust optimal centralized controller to mitigate the small signal instability in an islanded inverter based microgrid with active and passive loads

This paper proposes a robust optimal centralized controller to mitigate the small signal instability caused by the interaction between the control loops of rectifier interfaced active loads (RIALs) and the inverter interfaced distributed generations. The proposed controller is based on a robust linear quadratic regulator with prescribed degree-of-stability (LQRPDS). In order to reduce communication system burden and improve the flexibility and reliability of the proposed controller, a kalman state estimator has been designed. The optimal values of the diagonal weighting matrices of LQRPDS and kalman estimator are obtained by formulating a bi-objective optimization problem, which is solved using a fast and elitist multi-objective non-dominated sorting genetic algorithm (NSGA-II). A full dynamical linearized state space model of an islanded MG represented in synchronous (DQ) reference frame has been considered for analyzing the stability. However, schur balanced model order reduction technique has been used to reduce the complexity in the design of the controller. Eigenvalue analysis and time-domain simulations have been presented to demonstrate the robust performance of the controller under various load configurations as well as under step load disturbances. The efficacy of the proposed centralized controller is verified by comparing its performance with that of LQR based centralized controller.     

一种改进的低压电力线正交频分复用载波通信的最小均方信道估计算法

研究分析了正交频分复用(OFDM)通信中的最小均方(LMS)自适应信道估计算法,提出了一种改进的变步长LMS信道估计算法。该算法根据εm(k)、εm-1(k)自相关的时间平均估计及其均方误差(MSE),实现了对LMS算法步长的调整。仿真试验结果表明采用改进后的LMS信道估计算法的OFDM系统误码率明显降低,MSE降低了1/10,提高了算法的信道跟踪性能,且运算复杂度小,更适用于低压电力线OFDM通信系统。     

Beta‐order minimum mean‐square error multichannel spectral amplitude estimation for speech enhancement

In this paper, the minimum mean‐square error (MMSE) β‐order estimator for multichannel speech enhancement is proposed. The estimator is an extension of the single‐channel MMSE β‐order and multichannel MMSE short‐time spectral amplitude estimators using Rayleigh and Gaussian distributions for the statistical models under the assumption of a diffuse noise field where the noise is estimated independently across each of the microphones. Experiments are performed to evaluate the new estimator against the baseline single‐channel and multichannel estimators using various values of the β parameter and number of microphones along with different levels of noises as a function of the input signal‐to‐noise ratio. By the utilization of additional microphones, the multichannel MMSE β‐order estimator achieves performance gains in noise reduction, speech distortion, and speech quality as measured by the segmental signal‐to‐noise ratio, log‐likelihood ratio, and perceptual evaluation of speech quality objective metrics. Copyright © 2015 John Wiley & Sons, Ltd.     

Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

Toward adaptive robust state estimation based on MCC by using the generalized Gaussian density as kernel functions

In this paper, a generic formulation is proposed for robust state estimation (RSE) based on maximum correntropy criterion (MCC), leading to an adaptive robust state estimator. By using the generalized Gaussian density (GGD) as the kernel function, the proposed formulation theoretically unifies several existing RSE models, each of which is optimal for a specific type of measurement noise and error distribution. As the noise and error distribution is generally unknown ex-ante and time-varying in operation, a statistical learning scheme is proposed to heuristically identify the actual distribution type online. Afterwards, the optimal RSE can be properly selected so as to adapt to the variation of noise and error distribution types. Simulations are carried on a rudimentary 2-bus system and the standard IEEE-118 bus system, illustrating the correctness and effectiveness of the proposed methodology.     

一种OFDM时域导频插入的最小二乘估计方法

作为一种高效的传输技术,OFDM(正交频分复用)正被广泛地应用于下一代无线通信系统。本文采用了一种OFDM系统中低复杂性的LS(最小二乘方)信道估计算法,该算法采用在时域上插入导频数据,通过简单的降噪处理,来提高信道估计的精度。同时,提出了一种时域导频符号,该符号可根据信道估计的复杂度和精度灵活选取,具有很大的灵活性。另外,该种导频符号具有恒包络的特性,使信道估计能得到很好的时域发射PAR(峰均功率比)性能。仿真结果证实了该估计算法的有效性。     

Fast array algorithm for filtering of Markovian jump linear systems

This paper deals with numerical performance of linear minimum mean square error estimator for discrete‐time Markovian jump linear systems. We propose a fast array algorithm to compute this estimator in virtue of the reduction of the computational effort that it provides, if compared with the standard array algorithms. It is particularly useful for this case because the dimension of the linear minimum mean square error estimator increases proportionally to the number of Markovian states. Numerical examples are shown to illustrate the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

一种正交频分复用低压电力线通信系统的信道估计与迭代均衡策略

正交频分复用(orthogonal frequency division multi- plexing,OFDM)技术在低压电力线高速通信研究和应用领域获得了广泛重视。传统的OFDM系统通过对每个符号添加大于信道响应长度的循环前缀(cyclic-prefix,CP)来消除符号间干扰,使传输效率降低。提出一种低压电力线信息符号无循环前缀的OFDM通信系统的信道估计与迭代均衡策略,该策略采用m序列作为训练序列,利用其特殊的自相关特性,在系统接收端获得信道的时域响应,进而对无循环前缀的OFDM信息符号进行迭代均衡,以消除符号间干扰。仿真实验表明,该策略可提高传输效率、增强系统的抗噪性能、获得比传统OFDM系统更低的信道估计均方误差和误码率,且具有较低的计算复杂度。     

基于训练序列的光信道估计

大气信道的复杂性会引起信道状态参数的随机变化,这对通信系统性能有很大影响。为此,研究了基于训练序列的沙尘光信道估计,以m序列为训练序列,采用时分复用方式构建了一种基于训练序列的光信道估计方案。该方案利用训练序列中的0序列,构造了一种噪声方差的估计方法。在此基础之上,研究了最小均方误差(MMSE)准则下不同散射路径数和训练序列长度对估计均方误差(MSE)和系统误码性能的影响。研究结果表明,在小信噪比的大气信道中,MMSE准则下散射路径数不宜超过5,m序列长度选63为宜。     

A rate adaptive bit-loading algorithm for in-building power-line communications based on DMT-modulated systems

This paper deals with a variable rate discrete multitone modulation system for broadband power-line communications, based on the bit-loading algorithm proposed by Leke and Cioffi. In the proposed system a suitable least mean square channel estimator is considered, which is based on the insertion of a training sequence (TS). The proposed approach will be compared with the ideal channel estimates, showing its effectiveness. Moreover, different TS lengths will be compared. The system performance, expressed in terms of bit rate and bit-error rate, is derived by simulation with and without estimation errors. The propagation environment has been assumed as a frequency-selective multipath fading channel with additive colored Gaussian noise, according to the in-building networks model.     

采用RSSI模型的无线传感器网络协作定位算法

由于无线传感器网络定位成本较高,精度不能满足要求以及通信和计算开销过大等问题,提出一种针对定位各阶段实施误差抑制措施的接收信号强度指示(RSSI)测距的协作定位算法。测距阶段通过周期性测量获得模型动态参数,采用相对误差系数对RSSI测距进行校正,定位阶段则基于泰勒级数扩展线性最小二乘方法实现位置估计,采取残差加权法优化位置坐标,减小非视距(NLOS)的不利影响。引入协作定位,将符合要求的节点升级为参考节点参与定位计算,进一步提高定位覆盖率和精度。实验结果表明,所提算法精度接近基于真实坐标的泰勒级数扩展LS算法,相同条件下的精度远高于传统估计算法。节点最大定位误差为0.15,最小定位误差为0.08,网络节点平均定位误差为0.109,能够满足大规模无线传感器网络(WSN)的定位需求。     

改进的Chirp信号LMMSE参数估计算法

线性调频信号(Chirp信号)的参数估计是信号处理中长期研究的一个问题,其最大似然估计算法已经被提出并且得到广泛应用。然而在许多实际应用中,通常已经知道了被估计参数的分布,因此可以使用贝叶斯估计器的方法。在文中,通过加入先验知识,给出了参数的线性最小均方误差(Linear Minimum Mean Squares Error:LMMSE)估计算法。与传统的最大似然估计算法相比,此方法有更好的性能。同时,文中基于两种线性相位模型实现了LMMSE估计算法,一种是传统的线性相位模型,另一种是最近提出的最佳线性相位模型。实验结果表明基于最佳线性相位模型的估计器具有更好的性能。     

基于抗差最小均方估计的输电线路参数辨识

输电线路数学模型广泛运用于电力系统分析计算中,其参数的准确性与电网的安全稳定运行密切相关,广域测量系统的发展为获取输电线路参数提供了新的手段。针对目前参数辨识算法缺乏测量误差对辨识结果影响的研究,提出基于抗差最小均方估计（robust least mean squares,RLMS）的输电线路参数辨识算法,该算法以抗差函数代替传统最小均方估计算法中的均方误差,并通过自适应阈值法调节阈值,进而使得辨识算法在抗噪声方面具有较强的适应能力;对不同时刻的计算结果,提出了基于核密度估计和点估计法提取结果的统计特征,最后通过仿真分析与实测数据对比验证了所述算法的有效性。