特征参数自适应盲估计方法

 基于无线移动通信OFDM系统信道估计,提出了三种时域自适应特征参数估计最小均方盲方法——时变步长最小均方法(tvcpblms)、时变步长软判决最小均方法(tvcpsdwlms)、时变步长理想判决最小均方法(tvcpidwlms).这些方法通过对常规LMS算法步长进行自适应的科学设计以便跟踪特征参数变化,从而解决了常规LMS盲方法收敛速度慢、估计性能不高等缺点.仿真证明:对于不同的时延扩展、时间以及多普勒频移,这些方法均表现出了比常规方法更优的估计性能.同时,这些方法不仅可以估计无线移动通信系统信道特征参数,而且还可用于雷达、航天等多种领域估计其他特征参数.     

高精度无线移动通信信道盲估计方法

基于多载波OFDM系统,提出了一种新的时变步长修正软加权判决递归二乘信道估计盲方法(TVCPMSDWRLS).该法通过对常规算法步长进行自适应的科学设计以便跟踪信道特征变化,同时利用接收机判决信息函数修正权系数,解决了常规RLS盲方法收敛速度慢、信道估计性能不高的缺点.仿真证明:对于不同的时延扩展、时间以及信噪比(SNR),该法均表现出比常规方法更优的性能.同时,该法亦可用于估计通信、雷达、航天等领域的其他特征参数.     

MPSK调制系统中的自适应信道估计

本文研究了在MPSK调制方式下,不发送训练序列的商接判决自适应信道估计问题。为提高信道估计性能,提出采用软判决鉴相估值来调整加权最小均方(WLMS)和加权递归最小二乘(WRLS)自适应算法中的权值来进行信道估计,并研究了改进的软判决WRLS算法。结果表明,软判决加权LMS算法比一般LMS性能有很大提高,改进的软判决WRLS算法估计性能优于WRLS算法。     

OFDM系统自适应盲信道估计新方法

基于无线通信OFDM系统信道估计,提出了两种时域自适应盲估计方法。这些方法通过对极性(符号)LMS算法（SLMS）进行改进,改进算法有几方面优点,一是继承了极性LMS算法简单易实现的特性;二是解决了极性LMS收敛速度慢的缺点;最后结合自适应可变步长及步长调整策略,有效地提高了算法的估计性能。仿真给出了误差曲线以及归一化均方误差曲线,结果表明,和基于极性LMS盲估计方法相比,修正极性LMS和时变步长修正极性LMS盲估计方法均具有很快的收敛速度。由于采用了变步长技术,时变步长修正极性LMS盲估计方法具有更好的估计性能。      

Turbo编码OFDM系统中一种判决反馈的WLMS信道跟踪算法

在Turbo译码时对信息位和校验位都进行MAP译码,避免了传统基于译码再编码判决反馈信道估计和跟踪算法中反馈信号重构时的错误传播问题,降低了反馈信号的错误概率,提高了信道估计和跟踪的性能,解决了在信道变化较快条件下文献[8]提出的维纳LMS(WLMS)算法信噪比门限过高的问题。仿真结果表明,算法性能接近理想判决反馈时的性能限,与直接判决反馈的维纳LMS算法相比,在系统误比特率为10~(-4)条件下系统性能约提高2dB。     

稀疏水声信道判决反馈盲均衡算法研究

针对高速水声通信中信道的稀疏特性,提出了一种基于常数模准则的稀疏水声信道判决反馈盲均衡算法。该算法将改进的常数模算法与一种变化的判决反馈均衡器结构(部分反馈均衡器)有机结合,利用水声信道的稀疏特性,不但很好地实现了稀疏水声信道的盲均衡,而且简化了计算,易于算法的硬件实现。用典型稀疏水声信道进行了计算机仿真。结果表明,该算法性能稳定,计算量小,稳态均方误差低,整体性能与基于自适应LMS的稀疏迭代算法接近。该研究为高速水声通信中稀疏信道的均衡提供了一种可实现的方法。     

一种基于一阶扰动自适应特征值分解的多用户检测算法

本文提出了一种新的、适用于多径信道中多用户检测的盲自适应算法.这种算法使用基于一阶扰动的自适应特征值分解算法对信号空间进行分解,并使用最小均方(LMS)算法对信道进行盲自适应估计.实验和仿真证明,这种算法收敛速度快,稳态性能好,是一种较好的盲自适应多用户检测算法.     

WCDMA系统的迭代信道估计研究

本文针对WCDMA系统非连续导频情况,研究了采用Turbo编码的迭代信道估计技术,提出了一种基于LMS算法自适应滤波的信道估计方法.对信道估计值采用不同滤波器方法:维纳滤波、滑动平均滤波、FIR滤波、LMS自适应滤波的性能进行了比较,同时采用基于代价函数的方法对迭代信道估计进行了分析.理论分析和仿真结果表明对迭代信道估计所得信道估计值进行滤波的方法能够提高信道估计的精度,从而降低接收机的误比特率.     

基于改进的自相关匹配准则的盲均衡算法

本文针对多输入多输出(MIMO)信道模型,提出了一种新的信道盲均衡算法.该算法根据迫零条件,通过具有特殊自相关函数的前置滤波器的设计,采用改进的自相关匹配准则,实现了信道的完全均衡和信号的恢复.文章从理论上证明了该算法在存在高斯白噪声(AWGN)的非理想信道中,能够在信噪比变化的情况下满足迫零条件.通过与原有的自相关匹配方法、最小均方算法(LMS)的比较,模拟结果显示了本方法良好的性能.     

准正交空时分组码系统的一种盲信道估计方法

空时分组码(STBC)系统的经典信道盲估计方法,如子空间法(SS)等,都是基于接收端样本自相关矩阵的特征值分解(EVD)或奇异值分解(SVD)来实现信道估计的,而基于QR分解的信道盲估计方法是一种性能优良的新算法.文中将该算法应用到准正交空时分组码系统的信道估计中,结合准正交空时分组码的特性提出了一种新的信道盲估计算法.与以上经典的信道盲估计算法相比,文中提出的算法的计算量大为降低.同时Monte-Carlo仿真表明,当信噪比较低时,该算法比子空间法有更好的性能.     

基于模糊规则的低轨卫星信道自适应估计算法

低轨卫星通信功率控制需要有对信道增益及时的估计,而由于低轨系统链路增益的时变特性使传统的信道估计方法性能受到限制.基于模糊滤波理论,提出一种在功率控制中使用的自适应LMS信道估计算法,根据分析模型和经验数据建立信道增益及其滑动均值大小的模糊集,利用LMS算法调整模糊隶属函数的参数,使算法具有对信道时变特性一定的适应性.仿真结果表明了算法的有效性.     

时变多径MIMO信道中一种带自适应信道追踪的序列检测方法

该文针对时变多径MIMO信道,各MISO子系统首先分别采用混合MLSE(H-MLSE)处理,然后结合-幸存状态选择,提出了一种复杂度可控的、带自适应信道追踪的序列检测方法。与传统的MLSE算法相比,该方法具有3个显著特征:通过参数选择,可实现对算法复杂度的可控调节;通过嵌入的判决导向/LMS(DD/LMS)算法,对各幸存状态转移对应的幸存路径上的信道参数可实现接近零时延追踪;可部分采用并行处理技术来实现。对具有两条多径的2X4 MIMO时变信道通过数值仿真表明:当2时,该方法可获得满意的检测性能。     

Analysis of stochastic gradient tracking of time-varying polynomialWiener systems

This paper presents analytical and Monte Carlo results for a stochastic gradient adaptive scheme that tracks a time-varying polynomial Wiener (1958) system [i.e., a linear time-invariant (LTI) filter with memory followed by a time-varying memoryless polynomial nonlinearity]. The adaptive scheme consists of two phases: (1) estimation of the LTI memory using the LMS algorithm and (2) tracking the time-varying polynomial-type nonlinearity using a second coupled gradient search for the polynomial coefficients. The time-varying polynomial nonlinearity causes a time-varying scaling for the optimum Wiener filter for Phase 1. These time variations are removed for Phase 2 using a novel coupling scheme to Phase 1. The analysis for Gaussian data includes recursions for the mean behavior of the LMS algorithm for estimating and tracking the optimum Wiener filter for Phase 1 for several different time-varying polynomial nonlinearities and recursions for the mean behavior of the stochastic gradient algorithm for Phase 2. The polynomial coefficients are shown to be accurately tracked. Monte Carlo simulations confirm the theoretical predictions and support the underlying statistical assumptions     

Particle filtering based channel estimation in OFDM power line communication

Particle filtering （PF） algorithm has the powerful potential for coping with difficult non-linear and non-Gaussian problems. Aiming at non-linear, non-Gaussian and time-varying characteristics of power line channel, a time-varying channel estimation scheme combined PF algorithm with decision feedback method is proposed. In the proposed scheme, firstly the indoor power line channel is measured using the pseudo-noise （PN） correlation method, and a first-order dynamic autoregressive （AR） model is set up to describe the measured channel, then, the channel states are estimated dynamically from the received signals by exploiting the proposed scheme. Meanwhile, due to the complex noise distribution of power line channel, the performance of channel estimation based on the proposed scheme under the Middleton class A impulsive noise environment is analyzed. Comparisons are made with the channel estimation scheme respectively based on least square （LS）, Kalman filtering （KF） and the proposed algorithm. Simulation indicates that PF algorithm dealing with this power line channel estimation difficult non-linear and non-Gaussian problems performance is superior to those of LS and KF respectively, so the proposed scheme achieves higher estimation accuracy. Therefore, it is confirmed that PF algorithm has its own unique advantage for power line channel estimation.     

一种新型的基于深度学习的时变信道预测方法

针对高速移动正交频分复用系统,提出了一种新型的基于深度学习的时变信道预测方法。为了避免网络参数随机初始化造成的影响,本文方法首先基于数据与导频信息获取较理想的信道估计,利用其对BP神经网络进行预训练处理,以获取理想的网络初始参数;然后,基于预训练获取网络初始值,利用基于导频获取的信道估计对BP神经网络进行再次训练,以获取最终的信道预测网络模型;最后,本文方法基于该预测网络模型通过线上预测实现了时变信道的单时刻与多时刻预测。仿真结果表明,本文方法可以显著地提高时变信道预测精度,且具有较低的计算复杂度。     

Time-frequency 2-D LMS based long-range channel prediction for wireless OFDM systems

Adaptive modulation can optimize the spectrum efficiency and system performance with the channel state information achieved by the long-range channel prediction.To avoid re-estimating channel correlation function as the channel statioharity varies and to track the channel adaptively, LMS (Least-Mean-Square) based long-range channel prediction is discussed in the existing literature, but it needs long observation interval to reach the convergence.Given that all OFDM (Orthogonal Frequency Division Multiplexing) subcarriers have the identical time-domain correlation and sta- tionarity during the same time interval,this paper proposed a 2-D LMS based predictor which updates the filter weights in both time and frequency domain.The proposed scheme can effectively decrease the observation intervals and significantly speed up the convergence than the conventional LMS and Parallel LMS (PLMS).Complexity analysis and simulation results prove that the proposed scheme can improve the BER (Bit Error Rate) performance and spectrum efficiency with negligible complexity increase.     

一种水声通信Turbo均衡中的软迭代信道估计算法

 Turbo均衡技术是水声相干通信克服信道多径、消除码间干扰(ISI)的有效工具。Turbo均衡实际使用时需要对时变、多径信道进行良好的估计。为了提高信道估计的效果,该文基于时变横向滤波和相位旋转信道模型,提出一种水声通信Turbo均衡中的软迭代信道估计算法。该算法采用快速自优化最小均方算法得到各数据符号处的横向滤波器系数矢量并与二阶锁相环联合优化计算。通过仿真比较,该算法明显优于硬迭代信道估计算法,且相位估计性能优于其他文献中的软迭代信道估计算法。在海上试验中,水声通信距离5 km,方向近似垂直,接收阵起伏周期10 s,起伏幅度5 m左右,在此情况下进行数据采集。将该算法用于对海试数据的单通道Turbo均衡处理,实现无误码输出,验证了所提算法在软迭代信道相位估计方面的优势。     

MIMO-OFDM系统中一种基于自适应滤波的信道估计方法

该文提出了一种适用于MIMO-OFDM系统的基于自适应滤波器的信道估计方法,此方法在不需要任何信道统计信息的前提下,通过自适应滤波的方法对时变信道状态参数进行即时跟踪与估计。仿真结果表明该文提出的基于自适应滤波的信道估计方法,相比于不考虑噪声的基于LS算法的信道估计方法,MSE和BER性能均有很大的提高。其中基于LMS滤波器的信道估计方法具有计算复杂度小的特点;而基于RLS的信道估计方法具有收敛速度快,MSE和BER性能均优于基于LMS方法的特点。     

Trellis-coded differential unitary space-time modulation over flat fading channels

Coding and modulation for multiple-antenna systems have gained much attention in wireless communications. This paper investigates a noncoherent trellis-coded scheme based on differential unitary space-time modulation when neither the transmitter nor the receiver know the channel. In a time-varying flat Rayleigh fading environment, we derive differentially noncoherent decision metrics and obtain performance measures for systems with either an ideal interleaver or no interleaver. We demonstrate that with an ideal interleaver, the system performance is dominated by the minimum Hamming distance of the trellis code, while without an interleaver, the performance is dominated by the minimum free squared determinant distance (a novel generalization of the Euclidean distance) of the code. For both cases, code construction is described for Ungerboeck-type codes. Several examples that are based on diagonal cyclic group constellations and offer a good tradeoff between the coding advantage and trellis complexity are provided. Simulation results show that, by applying the soft-decision Viterbi decoder, the proposed scheme can achieve very good performance even with few receive antennas. Extensions to trellis-coded differential space-time block codes are also discussed.