基于子空间投影的信道阶数盲估计算法

根据联合阶数估计最小二乘平滑算法(J-LSS)中投影误差矩阵的特点,利用其零空间向量形成的特殊矩阵的秩与信道阶数的关系,分别构造2个阶数估计代价函数。将2个代价函数归一化后联合构建成新的代价函数,新的代价函数较使用单一代价函数提升了在低信噪比下的辨识率。仿真结果表明,与传统算法相比,该算法在较低的信噪比和小样本观测数据条件下,有很好的估计性能。     

高斯白噪声信道下8PSK信号信噪比估计

针对高斯白噪声信道下传统信噪比(Signal to Noise Ratio,SNR)估计算法适用的相位键控(PSK)调制阶数低、信噪比估计过程中数值计算系数大的问题,提出了一种新的8PSK信号盲信噪比估计算法。该算法理论分析了高斯白噪声信道下8PSK信号信噪比与中间观测量的数值关系,采用多项式拟合和观测量归一化处理的方式推导了二者的解析关系式。数值仿真表明该算法在-6~10 dB范围内,可取得良好的信噪比估计效果。     

一种改进的稀疏多径信道盲辨识算法

针对无线多径稀疏信道,利用信道有效近似思想,提出了一种改进的基于矩阵外积分解的信道盲辨识与盲均衡算法。算法首先利用改进的VIA信道阶数估计准则,对多径稀疏信道“有效部分”的阶数进行精确估计,然后利用改进的矩阵外积分解算法估计出信道冲激响应的“有效部分”,最后利用该估计结果对接收数据进行反卷积运算,恢复出发送信号。为了降低噪声以及信道冲激响应中的“零抽头”部分对信道盲辨识性能的影响,本算法对噪声方差估计方法进行了改进,提高了算法在中、低信噪比条件下的盲辨识性能。与现有算法相比,本算法不仅降低了对信噪比的要求,而且克服了基于LC准则的子空间算法(SSA, Subspace Algorithm)的相位偏转问题,其中噪声方差的估计方法也可应用于信噪比估计技术。仿真实验以及对SPIB微波信道测试结果验证了本文算法的有效性。      

多径衰落信道下导频辅助的OFDM定时估计算法

利用最大似然代价函数推导出了一种导频辅助的OFDM定时估计算法。在信道阶数未知情况下,对其导频采用循环前缀(CP)方式和补零(ZP)方式的性能做了比较分析。用ZP方式导频时,该算法克服了信道阶数未知时定时测度平台。仿真分析验证了本算法性能的优越性。     

一种新的多址信道有效阶数估计算法

针对多用户正交频分复用/空分多址(OFDM/SDMA)系统上行链路多址信道,基于噪声信道提出了一种新的信道有效阶数和信道冲激响应联合估计算法。该算法以最大似然为目标函数,构建了基于差分进化并行搜索信道有效阶数并进行信道冲激响应估计的联合框架。算法引入赤池信息量准则作为搜索阶数最优的评判函数,以提高信道有效阶数和信道冲激响应的估计精度。仿真验证了所提算法的有效性和可靠性,结果表明引入赤池信息量准则(AIC)在降低有效信道阶数估计误差的同时提高了时域最大似然信道估计器的性能。特别地,在误码率为10-5时,所提算法能够获得约1.5 dB的性能增益。     

利用特征极值比的盲信道阶数估计方法

确定性辨识方法是盲信道辨识的主流方法,然而确定性方法性能受信道阶数估计的严重影响。本文针对大多数信道阶数估计算法在坏信道条件下失效问题,分析子空间方法中噪声子空间矢量构成特殊矩阵的奇异性与信道阶数之间的关系,对该特殊矩阵最大特征值最小特征值的变化情况进行对比分析,利用特征极值的比值来反映信号子空间到噪声子空间的变化情况,从而提出特征极值比定理。针对观测数据有限且含噪声的实际应用条件,提出一种盲信道阶数估计算法,该算法以不同信道阶数的特征极值比作为参数构造目标函数,得到在真实信道阶数处目标函数取全局最大值,同时对该算法进行了复杂度分析。最后针对两种常用仿真信道参数对算法进行了验证,结果表明,在短数据和低信噪比条件下,本文算法能以较高的估计概率得到好信道和坏信道的有效阶数。      

基于均衡代价函数的信道阶数盲估计算法

针对信道阶数估计问题,利用单输入多输出(Single-Input Multiple-Output,SIMO)有限冲激响应(Finite Impulse Response,FIR)信道的结构特点和输入/输出信号的统计特征,提出了一种基于均衡代价函数的信道阶数盲估计算法.首先计算了归一化最小二乘均衡(Normalized Least Squares Equalization,NLSE)代价函数在理想条件下的理论渐近值,并指出其拐点与信道阶数之间的对应关系.然后分析了NLSE代价函数在实际条件下的近似值.最后引入了拐点优化因子,提出了一种基于NLSE代价函数拐点检测的信道阶数估计算法.理论分析和仿真结果表明,在信噪比(Signal-to-Noise Ratio,SNR)较低和信道首尾系数较小的情况下,该算法比现有其它方法具有更强的鲁棒性,可以获得更小的接收信号均衡误差.     

认知无线电中OFDM信号信噪比盲估计

针对认知正交频分复用(OFDM,orthogonal frequency division multiplexing)系统中低信噪比多径信道下传统的OFDM信号信噪比盲估计算法的估计性能差,计算复杂度高的问题,提出一种新的OFDM信号信噪比盲估计方法,该方法首先利用自相关函数的特性粗略估计出信道阶数,确定循环前缀部分中不受符号间干扰的数据区间,然后根据选定区间的数据的自相关函数值估计接收信号的信号功率,最后利用循环前缀数据为部分有用数据的复制这一特性估计出噪声功率,从而估计出接收信号的信噪比。仿真实验结果表明,提出的方法无需任何先验信息,在低信噪比多径信道下具有良好的估计性能,且计算复杂度低,更适合于认知OFDM系统。     

基于均值循环卷积特性的UWB信道盲估计算法

该文针对采用码片率抽头间隔的TH-PPM超宽带系统离散信道,利用接收信号的均值循环卷积特性,对UWB信道估计问题进行建模,结合UWB信道的稀疏簇结构,提出一种基于抽头探测的UWB信道盲估计算法,避免了无谓的零抽头估计,改善了算法性能。仿真表明:在低信噪比(0-15dB)的情况下,基于抽头探测算法的MSE比没利用信道结构特征的最小二乘算法平均低约5.5dB;在中等信噪比(15dB)的情况下,基于抽头探测算法的MSE比最小二乘算法平均低约3.5dB,同时基于抽头探测算法还能获得较好的SER(Signal-Error-Ratio)性能。     

一种基于序列相关的信噪比估计算法

针对多径信道,提出了一种基于序列相关的信噪比估计算法,利用本地序列与接收信号相关,并采用最小二乘估计法,精确地估计了接收信号幅度和噪声方差,得到了两径信道下信噪比的估计值。仿真结果表明该算法整体估计性能较好,特别适合于低信噪比条件下。在信噪比为-1dB时,与现有的频域和二阶矩四阶矩（M2M4,2-order and 4-order Moments）估计算法相比,该算法的归一化均方误差分别降低了0．09和0．2。     

A New Data Rotation Based CP Synchronization Scheme for OFDM Systems

In this paper, a new data rotation scheme for improving the symbol timing and carrier frequency offset (CFO) estimation of orthogonal frequency-division multiplexing (OFDM) systems is proposed. The new data rotation scheme intentionally introduces a cyclic shift after the inverse fast Fourier transform (IFFT) in the transmitter so that a higher energy cyclic prefix (CP) is obtained. This cyclic shift will not impair the orthogonality among the subcarriers and will only results in phase shift in the demodulated signal at the receiver. To recover the cyclic shift and for data detection, the scheme makes use of double differential encoding and decoding at the transmitter and the receiver. We analyze the performance of the new data rotation scheme by using order statistics theory. Our results show that the new scheme can provide a 1.6 dB gain in the performance of the CFO estimator and a 6 dB gain for the timing estimator at 15 dB SNR over AWGN channel, as well as a 6 dB gain in lock-in probability and a 4 dB gain in CFO performance at 5 dB SNR over frequency selective fading channel.     

Non data-aided SNR estimation of OFDM signals

This letter deals with the problem of non data aided (NDA) signal to noise ratio (SNR) estimation of OFDM signals transmitted through unknown multipath fading channel. Most of existing OFDM SNR estimators are based on the knowledge of pilot sequences which is not applicable in some contexts such as cognitive radio for instance. We show that it is possible to take advantage of the periodic redundancy induced by the cyclic prefix to get an accurate NDA SNR estimator. Numerical simulations highlight the benefit of the proposed method compared with the state of the art.     

SNR estimation in time-varying fading channels

Signal-to-noise ratio (SNR) estimation is considered for phase-shift keying communication systems in time-varying fading channels. Both data-aided (DA) estimation and nondata-aided (NDA) estimation are addressed. The time-varying fading channel is modeled as a polynomial-in-time. Inherent estimation accuracy limitations are examined via the Cramer-Rao lower bound, where it is shown that the effect of the channel's time variation on SNR estimation is negligible. A novel maximum-likelihood (ML) SNR estimator is derived for the time-varying channel model. In DA scenarios, where the estimator has a simple closed-form solution, the exact performance is evaluated both with correct and incorrect (i.e., mismatched) polynomial order. In NDA estimation, the unknown data symbols are modeled as random, and the marginal likelihood is used. The expectation-maximization algorithm is proposed to iteratively maximize this likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.     

Blind equalization of IIR channels using hidden Markov models andextended least squares

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization     

Frequency estimation of a single complex sinusoid using a generalized Kay estimator

This letter introduces a generalized version of Kay's estimator for the frequency of a single complex sinusoid in complex additive white Gaussian noise. The Kay estimator is a maximum-likelihood (ML) estimator at high signal-to-noise ratio (SNR) based on differential phase measurements with a delay of one symbol interval. In this letter, the corresponding ML estimator with an arbitrary delay in the differential phase measurements is derived. The proposed estimator reduces the variance at low SNR, compared with Kay's original estimator. For certain delay values, explicit expressions for the window function and the corresponding high SNR variance of the proposed generalized Kay (GK) estimator are presented. Furthermore, for some delay values, the window function is nearly uniform and the implementation complexity is reduced, compared with the original Kay estimator. For a delay value of two, we show that the variance at asymptotically high SNR approaches the Cramer-Rao bound as the sequence length tends to infinity. We also explore the effect of exchanging the order of summation and phase extraction for reduced-complexity reasons. The resulting generalized weighted linear predictor estimator and the GK estimator are compared with both autocorrelation-based and periodogram-based estimators in terms of computational complexity, estimation range, and performance at both low and high SNRs.     

Soft Decoding Assisted SNR Estimation Under Block Fading Channels for Orthogonal Modulations

The performance of the coded orthogonal modulation (OM) system under slow fading channels heavily depends on the estimation of the signal-to-noise ratio (SNR), including the fading amplitude and the noise spectral density. However, a relatively long packet of pilot symbols is often required to guarantee the accuracy of the SNR estimation, which makes it impractical in some situations. To address this problem, this paper proposes an iterative SNR estimation algorithm using the soft decoding information based on the expectation-maximization algorithm. In the proposed method, a joint iterative loop between the SNR estimator and decoder is performed, where the extrinsic information generated by the soft decoder is employed to enhance the estimation accuracy and the SNR estimated by the estimator is used to generate the soft information to the decoder. Also, no pilot symbols are needed to estimate the SNR in the proposed estimator. The Cramer–Rao lower bound (CRLB) of fully data-aided (FDA) estimation is derived to works as the final benchmark. The performance of the proposed algorithm is evaluated in terms of the normalized mean square errors (NMSEs) and the bit error rates (BERs) under block fading channels. Simulation results indicate that the NMSE of the proposed estimator reaches the CRLB of the FDA estimator and outperforms that of the approximate ML (ML-A) estimator proposed by Hassan et al. by 4.1 dB. The BER performance of coded OM system with the proposed estimation algorithm is close to the ideal case where the channel fading and the noise spectral density are known at the receiver.     

Channel estimation for nonlinear MIMO receiver with square envelope detection

Two practical channel estimation schemes, the moment‐based first‐and‐second moments and the simplified maximum likelihood estimators, are proposed for the MIMO/on–off keying system with square envelope detection applied for wireless sensor networks. Here, both the channel response and noise power are estimated simultaneously in comparison with other approaches in which the noise quantity is assumed to be known at the receiver. Hence, the developed estimators are more practical than those estimators without noise power estimation. Simulation results reveal that the system with both proposed schemes can achieve an excellent BER performance in a wide signal‐to‐noise ratio (SNR) range. More specifically, we observed that the simplified maximum likelihood estimator performed as well as the moment‐based first‐and‐second moments estimator for SNR greater than 7.5 dB, yet had much more decline at low SNRs. This study also investigated the effects of the numbers of receive antennas and transmit antennas on the system performance. Simulation results demonstrated that, at the BER of 10^?3, the 5 × 5 system had an improvement of 7 dB in SNR compared with the 3 × 3 system. Copyright © 2011 John Wiley & Sons, Ltd.     

基于阈值处理的TD-SCDMA下行多用户信道估计方法

 阈值后处理是针对TD-SCDMA系统中B Steiner信道估计器受加性噪声影响而提出的一种去噪方法,该方法误滤除信息径,保留噪声径.本文提出一种基于阈值处理的多用户信道估计方法,它基于某一下行用户会收到所有其它下行用户的训练序列这一特性,设定一个值,与各用户幅度加权值比较,去掉信噪比很小的用户对应的信道估计窗,将其余用户的信道估计窗进行平均,然后将平均后的结果再进行阈值后处理,抑制由噪声贡献的信道响应.仿真结果表明,提出的方法与直接进行阈值后处理法在同一误码率下,所需平均信噪比(SNR)降低1~4dB;在SNR低于5dB时,与复杂度高的加权合并法具有相同的误码率.     

Subspace-based noise variance and SNR estimation for MIMO OFDM systems

This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio （SNR） estimation algorithm for Multi-Input Multi-Output （MIMO） wireless Orthogonal Frequency Division Multiplexing （OFDM） systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.     

Non Data Aided SNR Estimation for OFDM Signals in Frequency Selective Fading Channels

This paper deals with the problem of non data aided (NDA) signal to noise ratio (SNR) estimation of OFDM signals transmitted through unknown multipath fading channel. Most of present day’s SNR estimators are based on the knowledge of pilot sequences which is not applicable in some contexts such as cognitive radio for example. Moreover in Multipath fading channels SNR also depends on frequency offset which is caused by mismatch between the oscillator in the transmitter and that in the receiver. Previous NDA SNR estimation schemes assumed a perfect synchronization at reception (i.e. τ = 0 and ${\varepsilon = 0}$ ) which results estimation of SNR with less accuracy. The frequency offset attenuates the desired signal and causes intercarrier interference, thus reducing the SNR. In this paper we propose a new NDA SNR estimator which uses periodic redundancy induced by the cyclic prefix, considering SNR degradation due to frequency offset ( ${\varepsilon}$ ).