High mobility channel estimation method based on improved basis expansion model

In this paper, the problem of high mobility channel estimation in the Long-Term Evolution for Railway (LTE-R) communication system is investigated. By using a Basis Expansion Model (BEM), the channel impulse response is modeled as the sum of several basis functions multiplied by coefficients. By estimating the basis function coefficients, the fast time-varying channel can be approximated. In order to reduce the estimation error resulting from the high frequency basis function, the Generalized Complex Exponential BEM (GCE-BEM) is modified to form an Improved GCE-BEM (IGCE-BEM) by adding a correction coefficient to the basis function. Moreover, an Improved Baseline Tilting (IBT) method is proposed to reduce the Gibbs effect. In addition, linear interpolation, Gauss interpolation, and three-order Hermite interpolation are adopted to obtain the channel impulse response at non pilot locations based on the channel estimation results at pilot positions. The simulation results show that the IGCE-BEM outperforms the CE-BEM and GCE-BEM in terms of the Normalized Mean Squared Error (NMSE). The IB T method is better than the BT method in reducing the Gibbs effect. In addition, combined with the IBT, the IGCE-BEM also has low NMSE under high moving speed and high noise power. The performance of the threeorder Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.

Deep learning-based time-varying channel estimation with basis expansion model for MIMO-OFDM system

For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed to model the time-varying channel,which converts the channel estimation into the estimation of the basis coefficient.Specifically,the initial basis coefficients are firstly used to train the neural network in an offline manner,and then the high-precision channel estimation can be obtained by small number of inputs.Moreover,the linear minimum mean square error(LMMSE) estimated channel is considered for the loss function in training phase,which makes the proposed method more practical.Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes,and it is robust to the fast time-varying channel in the high-speed mobile scenarios.

ESTIMATION OF BASIS EXPANSION MODELS FOR DOUBLY SELECTIVE CHANNELS

By analyzing the relationship between Basis Expansion Model (BEM) and Doppler spectrum, this letter proposed a quasi-MMSE-based BEM estimation scheme for doubly selective channels. Based on the assumption that the basis coefficients are approximately independent and have the same variance for the same channel tap, the quasi-MMSE estimation shows approximately optimal performance and is robust to noise. Moreover, it can avoid a high Peak-to-Average Power Ratio (PAPR) by using continuous pilots. Performance of the proposed estimation scheme has been shown with computer simulations.

一种低复杂度的正交时频空系统接收机设计

正交时频空(OTFS)调制可以将时间和频率选择性信道转换为时延-多普勒(DD)域的非选择性信道,这为高速移动场景建立可靠的无线通信提供了解决方案。然而,在车联网等复杂的多散射场景下,信道存在严重的多普勒间干扰(IDI),这给OTFS接收机信号的准确解调带来了极大的挑战。针对上述问题,该文提出一种联合稀疏贝叶斯学习(SBL)和阻尼最小二乘最小残差(d-LSMR)的OTFS接收机设计。首先,根据OTFS时域和DD域的关系,采用基扩展模型(BEM)将信道估计问题转换为基系数恢复问题,精准估计包括多普勒采样点在内的DD域信道。然后,提出一种高效的转换算法将基系数转换为信道等效矩阵。其次,将信道估计中估计得到的噪声,用于d-LSMR均衡器中进行信道均衡,并利用DD域信道矩阵的稀疏性实现快速收敛。系统仿真结果表明,与目前代表性的OTFS接收机相比,该文所提方案实现了更好的误码率性能,同时降低了计算复杂度。

Over-sampling basis expansion model aided channel estimation for OFDM systems with ICI

The rapid variation of channel can induce the intercarrier interference in orthogonal frequency-division multiplexing （OFDM） systems. Intercarrier interference will significantly increase the difficulty of OFDM channel estimation because too many channel coefficients need be estimated. In this article, a novel channel estimator is proposed to resolve the above problem. This estimator consists of two parts： the channel parameter estimation unit （CPEU）, which is used to estimate the number of channel taps and the multipath time delays, and the channel coefficient estimation unit （CCEU）, which is used to estimate the channel coefficients by using the estimated channel parameters provided by CPEU. In CCEU, the over-sampling basis expansion model is resorted to solve the problem that a large number of channel coefficients need to be estimated. Finally, simulation results are given to scale the performance of the proposed scheme.

基于多符号BEM的OFDM系统时变信道估计

针对基于扩展模型(BEM)的正交频分复用(OFDM)系统时变信道估计中频谱利用率不高的问题,提出了一种多符号BEM方法。相对于传统的单符号BEM方法,提出的方法减少了导频的子载波,并且通过基线倾斜技术抑制了傅里叶级数展开时出现的Gibbs现象。理论分析表明,基线倾斜技术引入后的信道模型误差在归一化多普勒频偏不大于((1/2)-(1/M))/(1+(N_g/N))时均有减小。数值仿真显示在相应的时变信道条件下,提出的方法的估计误差性能和误比特率(BER)性能相对传统方法有明显改善。

OFDM/OQAM系统中基于BEM信道模型的盲载波频偏估计算法

针对时频双选衰落信道下基于交错正交幅度调制的正交频分复用(orthogonal frequency division multiplexing with offset quadrature amplitude modulation,OFDM/OQAM)系统的频率同步问题,将双选信道建模为复指数基扩展模型,证明了存在载波频偏情况下OFDM/OQAM接收信号的二阶循环平稳特性,在此基础上,提出一种OFDM/OQAM系统载波频率偏差的盲估计算法。理论分析和仿真结果表明由该方法构造的估计器不仅能够有效地抵抗双选信道引起的衰落而且具有很好的抗噪性能,从而可以实现对载波频偏的稳健估计。

基于DFT-BEM模型的LOFDM系统双散射信道最大多普勒扩展估计

栅格正交频分复用(Lattice Orthogonal Frequency Division Multiplexing,LOFDM)系统凭借特殊的网格时频结构和更大的欧式距离特性,在快速移动环境下展现了卓越的抗时变、抗多径能力。最大多普勒扩展作为LOFDM系统自适应策略的重要参数之一,准确的最大多普勒扩展估计对于LOFDM系统发送信号设计以及自适应策略实现十分重要。本文针对LOFDM系统的特殊信号结构以及双散射信道的快时变特性,采用DFT-BEM信道模型近似快时变信道响应,结合快时变信道下LOFDM系统块传输接收实现,利用梳状导频辅助估计多普勒域平均信道频率响应,在此基础上利用信道响应估计值的时间相关函数实现基于F范数的信道最大多普勒扩展估计;并提出基于子空间的最大多普勒扩展估计算法,降低了噪声对最大多普勒扩展估计性能的影响,在低信噪比条件下有效改善了估计性能。

双选择性信道下发射分集MISO OFDM系统基于BEM的低复杂度信道均衡

在高速移动环境下,无线信道会同时经历时间选择性和频率选择性衰落,即所谓的快速时变信道,也称之为双选择性信道。最初的发射分集Alamouti编码方案是针对时不变平坦信道提出的,不能直接应用于快速时变信道。此外,OFDM系统在双选信道下遭受的载波间干扰(ICI)不可忽视。因此,发射分集MISO OFDM系统在双选择性信道下既节能又有效的信号恢复是有挑战的。本文基于双选择性信道的基扩展模型(BEM)表示,研究了一种有效的可动态分组的混合干扰消除(HIC)信道均衡方案。仿真结果表明,提出的方案,与传统的MMSE均衡相比,计算复杂度大大降低的同时性能显著提高,计算量的降低减少了能量消耗,达到节能的目的;与现有的关于发射分集的信道均衡方案相比,表现出性能和复杂度的较好折中;此外,在信道信息完美已知的假设下,随着移动速度的提高,误码性能没有损失。

基于BEM的非平稳双选信道估计方法

面向高速环境下的无线通信系统,针对高速信道的双选衰落和非平稳特性,提出一种基于基扩展模型(Basis Expansion Model,BEM)的贝叶斯滤波的信道估计方法.针对双选衰落特性,采用BEM信道模型,降低估计复杂度,消除子载波间干扰;针对非平稳特性,提出一种基于贝叶斯滤波的联合估计信道冲激响应与时变的时域自相关系数的信道估计方法.仿真分析表明,所提方法相较最小二乘法等传统方法在高速环境下能够提升估计精度和误码率性能.本方法特别适用于高速铁路的无线通信系统.

高速移动OFDM系统中基于位置信息的GCE-BEM时变信道估计方法

针对高速移动正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统,文中提出了一种基于位置信息的广义复指数基扩展模型(Generalized Complex Exponential Basis Expansion Model,GCE-BEM)时变信道估计方法。首先采用GCE-BEM方法对信道进行建模,将对信道的估计转换成对基系数的估计,一方面降低了计算复杂度,另一方面降低了载波间干扰,提高了信道估计精度;其次根据信道的稀疏性,利用列车的位置信息确定主要基系数,通过对主要基系数进行估计获取OFDM符号上所有载波的信道信息,进一步降低了相邻子信道的干扰与计算复杂度,提高了信道估计精度。理论分析与仿真结果表明,本文方法可以显著地提高信道估计精度,且具有低的计算复杂度。

Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System

Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

快时变信道下基于深度学习的OFDM系统信道估计

针对快时变信道的非平稳特性会造成信道估计性能变差的问题,在基扩展模型下提出了一种基于深度学习的信道估计算法,并将其应用于正交频分复用(orthogonal frequecy division multiplexing,OFDM)系统中。首先,根据快时变信道矩阵的局部相关特性,构建时频特征提取网络,利用卷积结构提取快时变信道在时域和频域的相关特征,并嵌入到下一级网络中进行特征的融合。其次,利用门控循环网络捕捉信道在不同符号处的变化相关性,在快时变信道环境下实现更准确的信道估计。仿真结果表明,与其他快时变环境下的信道估计算法相比,算法的估计性能提升明显;同时,网络的轻量化结构使算法的复杂度最低下降20%。

径向基函数-稀疏多项式混沌展开混合代理模型可靠性分析方法

为解决现有代理模型在可靠性分析中存在的普适性差、分析精度低的问题,提出一种融合径向基函数(Radial Basis Function,RBF)和稀疏多项式混沌(Sparse Polynomial Chaotic Expansion,SPCE)展开的混合代理模型,实现功能函数的快速准确预示,从而提高可靠性分析的工程适用性和计算精度。采用正交匹配追踪技术求解多项式混沌(Polynomial Chaotic Expansion,PCE)展开中的重要项,获得SPCE展开模型,并将其增广到RBF代理模型中,形成RBF-SPCE展开混合代理模型,提高代理模型的预测精度,进而结合蒙特卡洛模拟(Monte Carlo Simulation,MCS)方法开展复杂结构的可靠性分析。三个数值算例对比了所提方法与传统RBF方法及增广RBF方法的具体差异。结果表明,所提方法对结构可靠性分析具有更高的精度和效率。最后,汽车侧面碰撞的工程算例说明所提方法对复杂问题具有良好的工程适用性。

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。

OFDM系统中优化的泛化复指数基扩展模型

对正交频分复用系统中时间选择性信道的估计模型——泛化复指数基扩展模型(GCE-BEM)进行优化,根据多普勒频移的估计情况,提出了可以进行自适应系数调整的模型,通过选择固定系数或变化系数的GCE-BEM,在不增加计算复杂度的前提下能有效消除GCE-BEM模型的高频噪声的影响,可以减小信道的估计误差,提高了系统性能.

基于基扩展模型的UKF-RTSS高可靠鲁棒V2V信道估计

车联网应用场景对无线通信在带宽、时延、可靠性方面提出了更高的需求,特别是车辆对车辆(Vehicle to Vehicle,V2V)场景。针对V2V高速移动场景,时/频域选择性衰落(双选衰落)和非平稳特性给信道估计带来的技术挑战,该文提出了一种基于基扩展模型(Basis Expansion Model,BEM)的UKF-RTSS(Unscented Kalman Filter-Rauch-Tung-Striebel Smoother)信道估计方法。该方法采用BEM拟合快时变信道,将信道参数的估计转化为基函数系数的估计;通过无迹卡尔曼滤波(UKF),联合估计数据处信道冲激响应与时域自相关系数,用于追踪快时变的信道响应。为了进一步提升信道估计的精度,引入RTSS对后向信道状态信息进行信道估计和插值,与UKF构成了“滤波和平滑”结构的UKF-RTSS联合估计器。系统仿真分析表明,在不同速度的快时变条件下,所提方法相比其他经典方法具有更高的信道估计精度和鲁棒性,特别适用于车联网下的无线通信场景。

一种改进的快变信道展开模型

在高速移动正交频分复用系统(OFDM)中,信道的快速变化引起载波间干扰,降低系统性能,且使信道的准确估计变得更为困难.由于在接收端待估计的信道参数多于接收信号样点数,信道估计方程无确定解.为了解决这一问题,通常将快变信道展开为基函数叠加的近似表达形式,信道估计问题变成对展开系数的估计,待估计的参数大大减少.本文分析并比较了常用的基展开模型的特点,指出了它们的不足,并在此基础上提出一种改进的快变信道展开模型,该模型的核心思想是在过采样基础上通过基线补偿减小吉布斯效应的影响,从而减小展开误差.该模型性能与信道的统计性质无关,且在展开基的变化赶不上实际信道变化速度时,展开误差不会明显增大,优于CE-BEM,GCE-BEM,KL-MEM等模型,可用于高速移动OFDM系统的快变信道估计.

应用基扩展模型的混合信号单通道盲分离算法

针对传统最小均方误差逐幸存路径处理(LMS-PSP)单通道盲分离算法在时变信道下性能差的问题,提出一种基于基扩展模型逐幸存路径处理(BEM-PSP)的单通道盲分离算法。首先对接收到的部分混合信号进行LMS-PSP单通道盲分离,得到部分准确的信道冲激响应(CIR);然后结合时变信道下基于基扩展模型进行信道估计的思想,完成整个时间周期CIR的估计;最后采用Viterbi算法对混合信号进行序列估计,从而实现时变信道下混合信号的单通道盲分离。仿真结果表明,对于2路混合QPSK信号,在相同仿真条件下,BEM-PSP算法较LMS-PSP算法能降低50%的复杂度且能获得更好的性能,在20dB处的误码率可达4×10-2,而LMS-PSP单通道盲分离算法的误码率只能达到1×10-1,并且在同等过采样倍数下,该算法能获得更高的性能提升。

MIMO系统中的迭代时变信道估计

为了提高时变信道环境中MIMO信道估计的性能,利用Karhunen-Loeve基扩展模型(KL-BEM)建立MIMO系统中符合期望最大化(EM)算法框架的信号模型,从而得到MIMO时变信道的迭代估计方法.将EM算法应用于MIMO系统中进行迭代信道估计,一方面利用了EM迭代来提高信道估计的性能,另一方面利用了KL-BEM基函数的正交性来降低信道估计的运算复杂度.在2×2 MIMO系统下的仿真结果表明:算法经5次迭代即可收敛,而且迭代估计的信道脉冲响应与实际响应几乎重合;此外,迭代估计后系统的BER性能接近理想信道时的BER性能,在高信噪比区域,两者之间的差别在1 dB以内,比最小二乘信道估计有约2 dB的性能增益,可见迭代估计方法在时变信道条件下具有良好的估计性能.