MPSK信号的最大似然SNR估计方法

该文利用最大似然准则分别推导了对于MPSK信号的数据辅助SNR估计方法、判决指向SNR估计方法和一种新的盲信噪比估计方法。文章对这些算法的性能进行了分析和计算机仿真,并与其它一些SNR估计算法进行了比较。分析和仿真的结果显示数据辅助的SNR方法性能优越,很好地符合了信噪比估计性能下限(CRLB)。判决指向算法性能依赖于判决的准确程度,在高信噪比时性能较好;而在低信噪比条件时,特别是小于OdB以后其估计有较大偏差。新的盲SNR估计算法对于BPSK信号具有估计范围广、精度高和复杂度小的特点,但是当M增加时性能会明显下降。     

基于缓变信号消除的数字通信系统信噪比盲估计算法

针对认知无线电中的频谱分析问题,在软件无线电构架下给出一种数字通信系统信噪比盲估计模型.分析了数字通信系统中缓变区的存在性,并根据数字通信信号的循环平稳特性给出了信号缓变区的选择方法.提出了一种基于缓变消除的信噪比盲估计算法,该方法不需要通信系统发送目标波形.仿真分析表明,所提方法能够准确找到数字通信信号的缓变区,对各种数字通信系统都具有较好的信噪比估计性能.     

SIMO信道中基于奇异值分解的盲信噪比估计算法

信噪比（SNR）是现代通信信号处理中一个重要参数,许多算法需要它作为先验信息以获取最佳估计性能。针对单输入多输出（SIMO）系统的信噪比估计问题,本文提出了一种盲信噪比估计算法。该算法利用多路信号协方差矩阵的奇异值分解（SVD）,通过计算矩阵的最大特征值实现各路信号信噪比估计。该算法无需知道信号的先验信息,能够对加性高斯白噪声信道（AWGN）和多径信道下常用的数字调制信号进行信噪比估计。仿真结果表明该算法具有良好的估计性能。与单路信号中基于SVD信噪比估计算法相比,该算法无需估计信号空间与噪声空间维数,提高了估计精度,同时大大减小计算复杂度。      

负信噪比直扩信号伪码盲估计方法

给出了一种采用延迟相关积累和信号子空间分析实现对负信噪比直扩（DSSS）信号伪码盲估计的方法。计算机仿真结果表明，该方法可以在-15dB信噪比条件下检测信号并估计出伪码、载波频率等参数，估计结果可以实现非合作解扩解调。     

基于时频脊线的跳频参数盲估计

 为了估计未知跳频信号的参数,提出一种基于时频脊线的跳频参数盲估计方法,根据跳频信号频率的瞬变特性,通过对跳频信号时频脊线的小波变换,可以准确估计跳周期,进而估计其它跳频参数.理论分析和仿真结果表明,该方法可以在低信噪比下以较低的运算复杂度实现高精度的参数估计,整体性能优于现有的跳频参数盲估计方法.     

负信噪比同步CDMA信号伪码盲估计方法

为了实现对直接序列扩频通信信号的非协作接收，提出了一种新的基于子空间分析和盲信号处理算法的负信噪比同步码分多址（CDMA）信号伪码全盲估计方法。该方法突破了以往直接序列扩频信号伪码估计技术单用户假设的局限，更加贴近实际应用。理论分析和计算机仿真表明了该方法的有效性。     

基于BP神经网络的直扩信号扩频码盲识别

针对微弱直扩信号的盲检测与估计问题,在接收方未知发送方扩频序列的前提下 ,提出了一种恢复直接序列扩频（DSSS）信号扩频码的方法。该方法基于反向传播（B P）神经网络,它的输入是接收到的信号,而其期望输出是和输入相同的信号,根据误差反 向传播来有监督地调节神经网络,网络达到收敛时根据第二层权值的符号函数值即可盲估计 出扩频码序列。实验结果表明,即使是在负信噪比情况下,该方法也能得到一个很好的估计 效果。     

BPSK信号盲信噪比估计的一种新算法

对于MPSK信号而言，信噪比的估计可以直接转化成对信号模的估计问题。本文从最大似然的信号模估计出发，通过复高斯白噪声信道的角度考虑BPSK信号和2次方去数据调制的方法得出了一种针对BPSK信号的盲信噪比估计新算法，计算机仿真显示这种简单算法具有优良的估计性能。文章还对新算法产生高性能的原因进行了分析。     

一种基于经验模态分解的信噪比盲估计新算法

为了增强未知样式信号的信噪比估计性能,提出了一种基于经验模态分解（EMD） 的信号信噪比估计新算法,通过固有模态函数（IMF）分量平均周期判断信号与噪声界限。 给出了经验模态分解估计法的工作原理和流程图,分析了经验模态分解估计法的性能。仿真 结果表明,与信号空间分解法一样,经验模态分解估计法能够实现盲信号信噪比估计,后者 估计均方误差比前者要小,在0 dB信噪比下均方误差不超过0.3 dB。     

短码DS-SS信号扩频序列及信息序列联合盲估计方法

研究了短码DS-SS信号的扩频序列及信息序列联合盲估计问题。首先,利用双信息符号周期、间隔一信息符号周期的时间窗对接收信号进行重组,并形成接收信号矩阵。然后,利用奇异值分解联合盲估计信号的扩频序列与信息序列。该算法在失步时间未知、低信噪比条件下利用单一矢量空间盲估计扩频序列和信息序列。不但不受扩频序列类型的限制,而且避免了传统特征值分解盲估计算法利用2个矢量空间组合扩频序列时存在的相位模糊问题,提高了盲估计性能。最后仿真验证了算法的有效性。     

Optimal blind carrier recovery for MPSK burst transmissions

The paper introduces and analyzes the asymptotic (large sample) performance of a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of carrier phase, frequency offset, and Doppler rate for burst-mode phase-shift keying transmissions. An optimal or "matched" nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged blind NLS estimators is developed. The asymptotic variance of these estimators is established in closed-form expression and shown to approach the Cramer-Rao lower bound of an unmodulated carrier at medium and high signal-to-noise ratios (SNR). Monomial nonlinear estimators that do not depend on the SNR are also introduced and shown to perform similarly to the SNR-dependent matched nonlinear estimator. Computer simulations are presented to corroborate the theoretical performance analysis.     

Code acquisition in transmit diversity DS-CDMA systems

In this paper, two code acquisition schemes are studied for use in conjunction with transmit diversity direct-sequence code-division multiple access (DS-CDMA). One is a training-based single-user maximum-likelihood (SUML) estimator, which can achieve code acquisition very fast at low computational complexity. The other is a blind estimator based on the multiple signal classification (MUSIC) algorithm. Two recently proposed transmit diversity schemes known as orthogonal transmit diversity simulcast (OTD-S) and space-time selective spreading transmit diversity (STSTD) are considered. While the advantages of transmit diversity from the detection standpoint are well known, less is known about how code acquisition performance is affected by the use of transmit diversity. Through the analysis in this paper, it is proven that the SUMI. estimator should give the same performance in both the OTD-S and STSTD schemes in a single-user environment. In a multiple-user environment, simulation results show that the STSTD system offers slightly better code acquisition performance. It is also seen that the SUML estimators provide significantly better code acquisition performance than the MUSIC estimators in either transmit diversity system. From the standpoint of robustness to carrier frequency offset, it Is found that the training-based SUML estimator is very sensitive to frequency offset, while the MUSIC estimator is quite robust. A simple frequency offset estimator to be used in conjunction with the SUML estimator is also proposed and is shown to make the timing estimator quite tolerant of substantial frequency offsets.     

An approximate maximum likelihood estimator for SNR jointly using pilot and data symbols

A novel maximum likelihood-based estimator for signal-to-noise ratio (SNR) is derived. Previous SNR estimators are mainly based on using either the pilot symbols or the data symbols. However, in a practical communication system, a frame usually consists of both pilot and data symbols. In this work, a new SNR estimator that uses all available symbols (pilot and data) in a frame is developed for binary phase shift keying signals. The performance of this estimator is examined. Numerical results are presented to show the potential improvement obtained by using this new estimator.     

一种新的基于改进PASTd的中频信号盲信噪比估计算法

该文提出一种加性高斯白噪声信道下基于改进的紧缩投影近似子空间跟踪(PASTd)的中频信号盲信噪比估计算法。将Gram-Schmidt正交化过程引入到PASTd中,使计算得到的特征向量相互正交,从而保证算法具有更好的收敛性能。对MPSK(M=2,4,8)信号和MQAM(M=16,64,128,256)信号进行了大量计算机仿真,结果表明该算法性能稳定,并且当信噪比变化范围为5dB到25dB时,所得到的估计偏差小于1dB,估计标准差在0.3以内。与基于特征值分解的算法相比,能够在得到精确估计结果的同时,大大减小运算复杂度。     

Performance analysis of end-to-end SNR estimators for AF relaying

Many existing signal-to-noise ratio (SNR) estimators were designed and evaluated for conventional one-hop communications systems. However, for a relaying system, it is the end-to-end SNR that determines the system performance. In this paper, we will fill this gap by evaluating the performances of the existing SNR estimators in a dual-hop relaying system used for each hop. The probability density functions of the SNR estimators are first derived, whose parameters are fitted as functions of the sample size and the true value of SNR. Using them, the cumulative distribution functions of the end-to-end SNR and the bit error rate performance for a relaying system are derived. Numerical results show that the squared signal-to-noise variance estimator has the best performance for small SNRs and the second-order fourth-order moments estimator has the best performance for large SNRs, while the signal-to-variation ratio estimator has the worst performance, among the existing SNR estimators, for AF relaying systems.     

Performance of robust symbol‐timing and carrier‐frequency estimation for OFDM systems

In recent years, many maximum likelihood (ML) blind estimators have been proposed to estimate timing and frequency offsets for orthogonal frequency division multiplexing (OFDM) systems. However, the previously proposed ML blind estimators utilizing cyclic prefix do not fully characterize the random observation vector over the entire range of the timing offset and will significantly degrade the estimation performance. In this paper, we present a global ML blind estimator to compensate the estimation error. Moreover, we extend the global ML blind estimator by accumulating the ML function of the estimation parameters to achieve a better accuracy without increasing the hardware or computational complexity. The simulation results show that the proposed algorithm can significantly improve the estimation performance in both additional white Gaussian noise and ITU‐R M.1225 multipath channels. Copyright © 2008 John Wiley & Sons, Ltd.     

一种LOFDM系统定时和频偏的盲估计算法

基于网格正交频分复用(LOFDM)信号的周期平稳性,该文提出一种LOFDM系统定时和载波频率偏差的盲估计算法。理论分析和仿真实验证实由该算法构造的估计器能够有效地对抗频率选择性慢时变信道引起的衰落;在信道噪声广义平稳的情况下,估计器性能与信噪比无关,于是估计器在低信噪比条件下也能很好地工作;另外,符号定时和频率偏差估计器的性能互不影响。     

Detection performance of the reduced-rank linear predictor ROCKET

This paper assesses the frequency detection capabilities of a new signal-dependent reduced-rank linear predictor applied to autoregressive spectrum estimation. The new technique is called reduced-order correlation kernel estimation technique (ROCKET). Its detection performance is examined by comparison to a full-rank autoregressive (FR-AR) estimator and two reduced-rank principal component autoregressive (PC-AR) estimators based on both the standard signal-independent version and a modified signal-dependent method. The performance of the new autoregressive estimator is also compared as a function of rank to the popular pseudo-spectrum estimator MUSIC. The performance metrics examined are the probability of detection (P/sub D/) and the false alarm rate (FAR) of detecting the spatial frequencies of plane waves impinging on a uniform line array in additive white Gaussian noise. These metrics are studied as a function of subspace rank, sample support, and signal-to-noise ratio (SNR). Simulations show that the signal-dependent reduced-rank estimators significantly outperform both the signal-independent version of PC-AR and the FR-AR estimator for low sample support and low SNR environments. One notable characteristic of ROCKET that highlights its distinct subspace selection is its performance as a function of subspace rank. It is observed that for equal powered signals, its peak performance is nearly invariant to signal rank and that at almost any subspace rank ROCKET meets or exceeds FR-AR performance. This provides an extra degree of robustness when the signal rank is unknown.     

QPSK数字接收机中的SNR估计的新方法

通过对基于判决反馈的信噪比最大似然估计推导过程的分析,得出:判决反馈最大似然估计得到的估计值是有偏估计;利用基带数据的高阶矩特性,可以获得渐近无偏估计。该文提出了两种新的SNR的迭代求解方法。一种是基于NDA(Non-Data-Aided)最大似然估计的梯度迭代求解方法,这种方法与其它迭代方法相比,具有更好的收敛性能。另一种是基于统计参量的迭代方法,它不需要对输入数据进行存储;而且在相同的信噪比估计性能下,与其它迭代运算相比,运算量大大降低,尤其适合于低信噪比下信噪比估计要求高的应用中。最后,文章对比了几种SNR估计子的性能与运算量。