基于直接判决和导频跟踪的OFDM系统快时变信道估计

提出了一种基于直接判决的OFDM系统的快时变信道估计方法。采用了直接判决算法进行信道估计，并从中选择有效的估计结果，联合导频信号进行信道跟踪。将基于训练序列的信道估计结果作为直接判决算法的初始值，利用传输信号直接判决的统计特性进行了信道估计，并利用改进的导频算法进一步地跟踪信道在时间上的变化。Simulink仿真结果表明，该估计算法适用于时变信道，比基于导频的信道估计方法和基于训练序列的信道估计方法效果都要好。     

基于BP神经网络的OFDM系统信道估计

针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中传统信道估计算法复杂度高或估计精度低的问题,给出一种基于反向传播(Back Propagation,BP)神经网络的信道估计方法.采用Simulink仿真工具构建OFDM信号采集平台,建立了基于BP神经网络的OFDM系统信道估计模型,并以均方误差和误码率为主要评价指标,分析了不同网络参数和导频数量对信道估计性能的影响.仿真结果表明,与传统信道估计算法相比,基于BP神经网络的信道估计算法可以提供更优的系统性能,而且可以减少导频数量,提高频带利用率.     

一种适用于终端移动的OFDM无线局域网的信道估计方法

本文提出了一种适用于具有移动终端的OFDM无线局域网的信道估计方法.该方法采用了卡尔曼滤波算法进行了信道估计,并利用导频进行信道跟踪.将基于训练序列的信道估计结果作为Kalman滤波器的初始值和观测值,用基于导频的信道估计结果来计算Kalman滤波器参数.并利用导频进一步跟踪信道在时间上的变化.Simulink仿真结果表明,该算法比基于导频的信道估计方法和基于训练序列的信道估计方法效果都要好.     

利用ICI自消除改善OFDM系统时变信道估计与均衡的性能

正交频分复用(OFDM)系统受到时变信道的影响,产生载波间干扰(ICI),导致系统性能严重下降。消除ICI最有效的方法是频域均衡,但这需要准确的信道估计。基扩展模型(BEM)能够准确逼近时变信道,从而将对信道的估计转化为对少量模型参数的估计。然而,在使用导频符号对参数进行估计时,来自相邻非导频符号的ICI干扰,却严重影响了估计的准确性。为此,通过分析子载波所产生的ICI系数的变化特性,该文提出采用ICI自消除方法来减小参数估计中所存在的ICI项,以提高估计的准确性,从而改善时变信道估计与均衡的性能。仿真结果验证了该方法的有效性,相比单纯基于BEM的信道估计方法,能够得到更准确的时变信道,以及更好的ICI消除效果。     

一种新的MIMO-OFDM系统自适应快时变信道估计算法

为了能够获得较精确的快时变信道估计,利用模糊模型拟合快时变信道,提出了一种新的信道估计算法。算法采用自适应技术进行导频子载波频域传输函数模型参数的识别,然后通过插值拟合全部信道的频域传输函数。仿真结果表明,在系统多普勒频移小于0.1的情况下,信道估计的MSE性能得到改善。     

存在反馈延迟的短波MIMO预编码系统设计

利用基于码本的预编码MIMO技术去提升短波通信的空间分集增益,短波天波信道利用电离层的一次或多次反射进行远距离通信,信号传播延时较大,为了消除反馈延时对预编码MIMO系统性能的影响,文中基于Kalman滤波器提出一种信道估计与预测模型,自适应地跟踪短波时变信道,接着根据ITS短波时变信道模型提取信道时域特征,估计出预测模型参数,通过与传统无信道预测的预编码方案进行对比,方案能够有效降低反馈延迟对短波MIMO预编码系统性能的影响。     

一种基于径向基函数网络的OFDM信道估计及跟踪方法

本文提出了一种基于径向基函数网络(RBFN)的正交频分复用(OFDM)系统信道估计及跟踪算法。该方法采用分散导频,并将RBFN网络参数相应地分成子集,依次利用导频子集对网络参数子集进行训练、更新,RBFN在学习与逼近信道响应时利用了OFDM信道的时频相关性。仿真结果表明该信道跟踪方法具有良好的误码率性能,且对快衰落有很强的鲁棒性。     

基于最优导频的自适应Kalman滤波信道估计方法

针对时变信道中的子载波间干扰(ICI)和噪声的统计模型不准确引起的滤波发散问题,介绍了一种基于最优导频预滤波的自适应Kalman联合算法。该算法通过使用最优导频滤除ICI,获得理想信道初始状态,然后将其作为Kalman滤波初始信息在时域上进行自适应Kalman信道估计。最后仿真实验表明,和传统的基于导频的Kalman滤波(KF)算法相比,该方法能有效抑制KF发散和改善信道估计精度。     

OFDM系统中基于导频的时变信道估计

该文对OFDM系统中基于导频的时变信道估计进行了研究,采用分组导频对一个OFDM符号间隔内某些时刻的信道冲击响应值进行估计,提出一种利用低通插值估计一个符号间隔内其他时刻的信道值的方法。仿真结果表明该文采用的低通插值方法所得到的估计均方误差要小于用线性插值或直接用信道模型产生的系数进行插值所得到的估计均方误差。     

MC-CDMA系统基于子空间跟踪的导频辅助信道估计

基于导频辅助的MC-CDMA系统信道估计方法通过在确定的时频位置发送已知的导频符号来获取信道参数,通常包括导频位置的最小二乘估计和内插滤波两步。为了提高导频位置信道频域特性的估计精度,使得后续内插滤波的精度相应得到提高,本文利用基于Givens旋转的子空间跟踪方法构成了对信道的时变参数的估计和跟踪,得到了较为准确导频信道估计。仿真表明,我们给出的基于子空间跟踪的信道估计方法具有更好的稳定性和更高的估计精度。     

高速移动环境下基于深度学习的信道估计方法

针对高速移动环境下信道快时变、非平稳特性导致下行链路信道估计性能受限的问题,本文提出一种基于深度学习的信道估计网络,即ChanEstNet.ChanEstNet使用卷积神经网络（Convolutional Neural Network,CNN）提取信道响应特征矢量和循环神经网络（Recurrent Neural Network,RNN）进行信道估计.我们利用标准的高速信道数据对学习网络进行离线训练,充分挖掘训练样本中的信道信息,使其学习到高速移动环境下信道快时变和非平稳的特点,更好的跟踪高速环境下信道的变化特征.仿真结果表明,在高速移动环境下,与传统方法相比,所提信道估计方法计算复杂度低,性能提升明显.     

Cloud security situation prediction method based on grey wolfoptimization and BP neural network

Aiming at the accuracy and error correction of cloud security situation prediction, a cloud security situation prediction method based on grey wolf optimization (GWO) and back propagation (BP) neural network is proposed.Firstly, the adaptive disturbance convergence factor is used to improve the GWO algorithm, so as to improve theconvergence speed and accuracy of the algorithm. The Chebyshev chaotic mapping is introduced into the positionupdate formula of GWO algorithm, which is used to select the features of the cloud security situation prediction dataand optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then, the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm toincrease the learning efficiency and accuracy of BP neural network. Finally, the real data sets of Tencent cloudplatform are predicted. The simulation results show that the proposed method has lower mean square error (MSE)and mean absolute error (MAE) compared with BP neural network, BP neural network based on genetic algorithm(GA-BP), BP neural network based on particle swarm optimization (PSO-BP) and BP neural network based onGWO algorithm (GWO-BP). The proposed method has better stability, robustness and prediction accuracy.     

Artificial Neural Network Channel Estimation Based on Levenberg-Marquardt for OFDM Systems

The many advantages responsible for the widespread application of orthogonal frequency division multiplexing (OFDM) systems are limited by the multipath fading. In OFDM systems, channel estimation is carried out by transmitting pilot symbols generally. In this paper, we propose an artificial neural network (ANN) channel estimation technique based on levenberg-marquardt training algorithm as an alternative to pilot based channel estimation technique for OFDM systems over Rayleigh fading channels. In proposed technique, there are no pilot symbols which added to OFDM. Therefore, this technique is more bandwidth efficient compared to pilot-based channel estimation techniques. Also, this technique is making full use of the learning property of neural network. By using this feature, there is no need of any matrix computation and the proposed technique is less complex than the pilot based techniques. Simulation results show that ANN based channel estimator gives better results compared to the pilot based channel estimator for OFDM systems over Rayleigh fading channel.     

快时变环境下 OFDM系统中的信道估计

在OFDM系统中,怎样对快时变信道进行较为准确的估计是一个具有挑战性的课题。该文在利用信道基扩展模型的基础上,提出了一种适合于快时变环境下OFDM系统的信道估计方法,并且依据使估计的均方误差最小的准则,推导了相应最优的导频序列,包括最优的导频取值和最优的导频分布。可以证明,最优的导频序列是由一些相邻的等间隔等能量的子序列构成,每个子序列的导频之间满足一定的相位关系。仿真结果表明了所提估计算法在快时变环境下的有效性和采用所推导的最优导频序列进行估计的优越性。     

分布式机器学习在RIS辅助的无线信道估计中的应用

无线信道估计是部署可重构智能超表面(Reconfigurable Intelligent Surface, RIS)辅助通信系统的关键与前提,然而下行链路传输环境下信道估计困难且导频开销大是对智能超表面辅助通信的重大挑战。针对以上问题,提出了一种基于分布式机器学习(Distributed Machine Learning, DML)训练模型的区域交集切换方案。首先,建立了一个多用户共享的下行信道估计神经网络,通过DML技术协同用户与基站训练网络模型。其次,搭建分层次神经网络结构对用户区域信道进行分类和特征提取。最后,针对用户处于相邻信道交集位置问题采用特征区域模型融合。仿真结果表明,基于区域交集的DML模型方案能在减少信道训练导频开销的同时最大化信道估计的精准性能。     

用于电力数据管理分析的负荷预测与异常检测

构建了一个电力数据管理分析系统,并设计了电力负荷预测算法和异常数据检测算法问题.首先,针对BP神经网络在预测电力负荷存在的因初始权值与阈值设置影响估计精度的问题,提出利用粒子群优化BP神经网络网络参数,提高了预测算法的收敛速度与预测精度;然后,针对电力数据异常检测算法效率较低的问题,提出了基于改进谱聚类的异常数据检测算...     

基于宽深超分辨率网络的信道估计方法

在正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM) 系统中由于快衰落导致信道特征不连续,常规的信道插值方法无法准确反应导频与整个信道之间的关联性。针对这一问题,提出了一种基于宽深超分辨率（Wide Deep Super-resolution,WDSR）网络的信道估计方法,把导频值通过最小二乘估计（Least Squares,LS）初步插值,再通过WDSR网络再次放大重构整个信道的响应。将信道估计插值上采样替换成初步插值和图像超分辨率上采样两步。仿真结果表明,与超分辨率卷积神经网络(Super-resolution Convolutional Neural Network,SRCNN)信道估计算法相比,在不同种类的信道以及导频数下WDSR信道估计方法均方误差性能提升约4.6 dB。     

Fading channel modelling using single-hidden layer feedforward neural networks

Due to the complexity and extensive application of wireless systems, fading channel modeling is of great importance for designing a mobile network, especially for high speed environments. High mobility challenges the speed of channel estimation and model optimization. In this study, we propose a single-hidden layer feedforward neural network (SLFN) approach to modelling fading channels, including large-scale attenuation and small-scale variation. The arrangements of SLFN in path loss (PL) prediction and fading channel estimation are provided, and the information in both of them is trained with extreme learning machine (ELM) algorithm and a faster back-propagation (BP) algorithm called Levenberg-Marquardt algorithm. Computer simulations show that our proposed SLFN estimators could obtain PL prediction and the instantaneous channel transfer function of sufficient accuracy. Furthermore, compared with BP algorithm, the ability of ELM to provide millisecond-level learning makes it very suitable for fading channel modelling in high speed scenarios.     

Diversity and channel estimation using time-varying signals and time-frequency techniques

We propose the use of time-varying (TV) signaling in modulation schemes to provide multiuser detection and multipath diversity in TV wireless channels. Specifically, we design an orthogonal linear chirp modulation scheme that is based on assigning different users with optimally designed parameters in order to reduce multiple-access interference. We also derive conditions on the parameters of the modulation signals to achieve multipath diversity. Furthermore, we propose the use of TV pilot signals with nonlinear instantaneous frequency and matched time-frequency (TF) techniques to estimate fast-fading channels with unknown state information. The proposed algorithm simplifies to the estimation of the parameters of multiple linear chirps, which we perform using the modified matching pursuit decomposition. We compare our estimation method with the use of pilot signals with linear instantaneous frequency, which we implement using the reassigned spectrogram. The proposed modulation scheme is applied to a frequency-hopped code-division multiple-access system for which we demonstrate improved performance when compared with frequency-shift-keying (FSK) modulation due to the designed multipath diversity and low multiple-access interference. Our simulations also demonstrate the increased estimation performance when pilot signals with nonlinear structures are used instead of linear structured ones to estimate TV channel parameters.