Adaptive group-blind multiuser detection based on a new subspacetracking algorithm

Wang and Host-Madsen (see IEEE J. Select. Areas Commun., vol.17, p.1971-84, 1999) developed group-blind multiuser detectors for use in code-division multiple-access (CDMA) uplink environments in which the base station receiver has the knowledge of the spreading sequences of all the users within the cell, but not that of the users from other cells. Yu and Host-Madsen (see Proc. IEEE Vehicular Technology Conf. (VTC99), Houston, TX, p.1042-46, 1999) later developed an adaptive version of this detector for synchronous CDMA channels. We develop a new low-complexity, high-performance subspace tracking algorithm and apply it to adaptive group-blind multiuser detection in asynchronous multipath CDMA channels. The detector can track changes in the number of users and their composite signature waveforms. We present steady-state performance as well as the ability of the receiver to track changes in the signal subspace. We also address the performance gain of the group-blind detector over its blind counterpart for this application     

SNR估计误差对BICM-ID系统性能影响的研究

在BICM-ID(Bit-Interleaved Coded Modulation with Iterative Decoding,BICM-ID)系统或其他级联码系统中实现迭代检测接收机需要对信道的信噪比(SNR)进行估计。该文研究信噪比估计误差对BICM-ID系统性能的影响,分析各种译码算法对信噪比估计误差的敏感程度。理论分析和仿真结果表明max-log-APP算法对信噪比估计具有最好的鲁棒性,log-APP及其他简化算法对SNR估计误差的鲁棒性在SNR过估计时比SNR欠估计时好,原因在于各个算法所采用的修正函数不同。满足算法性能没有大的损失的SNR估计误差精度范围随着信道条件的改善而增大,通常在-2dB到+6dB之间。因此在非理想的信道状态信息条件下,max-log-APP算法是BICM-ID系统的最佳译码算法。     

非理想信噪比估计对BICM-ID系统性能的影响

该文给出一种直观的信噪比估计非理想时的先验信息建模,使用外信息转移图较为全面地分析了非理想信噪比估计对带迭代译码的比特交织编码调制系统性能的影响,分析结果表明非理想信噪比估计对典型的星座类型和标号在高斯和瑞利信道下的影响相似;通过对不同帧长的系统性能比较可知,长帧下信噪比估计偏小在-3dB以下时性能会大大下降,而估计偏大则没有影响;而对于中短帧而言,在相同的偏差下,偏小的影响要大于偏大的影响;此外,还给出由拟合得到的8PSK高斯信道和16QAM高斯与瑞利信道下的信噪比估计算法,最后通过系统仿真验证了该算法的有效性。     

并行级联频域判决反馈(DFE)均衡策略及不匹配译码

该文提出了一种使用并行级联的turbo类型判决反馈均衡器(DFE)结构,用于抑制线性码间干扰。基于该结构又提出了一种低复杂度的基于频域迭代块判决反馈检测(Turbo Block DFE, TBDFE)算法。该并行策略比传统的块迭代判决反馈均衡算法(Iterative Block DFE, IBDFE)有更大的性能增益,且在信道估计存在相同的一定误差时,该结构与IBDFE的性能增益比没有估计误差时更大。该文还研究了SNR估计误差对TBDFE算法与传统IBDFE算法的影响,结果表明二者对SNR的过估计比欠估计均为敏感。     

Speech Enhancement Combining Optimal Smoothing and Errors-In-Variables Identification of Noisy AR Processes

In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. (1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. (2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. (3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests.     

Sparse least mean fourth algorithm for adaptive channel estimation in low signal‐to‐noise ratio region

Both least mean square (LMS) and least mean fourth (LMF) are popular adaptive algorithms with application to adaptive channel estimation. Because the wireless channel vector is often sparse, sparse LMS‐based approaches have been proposed with different sparse penalties, for example, zero‐attracting LMS and L_p‐norm LMS. However, these proposed methods lead to suboptimal solutions in low signal‐to‐noise ratio (SNR) region, and the suboptimal solutions are caused by LMS‐based algorithms that are sensitive to the scaling of input signal and strong noise. Comparatively, LMF can achieve better solution in low SNR region. However, LMF cannot exploit the sparse information because the algorithm depends only on its adaptive updating error but neglects the inherent sparse channel structure. In this paper, we propose several sparse LMF algorithms with different sparse penalties to achieve better solution in low SNR region and take the advantage of channel sparsity at the same time. The contribution of this paper is briefly summarized as follows: (1) construct the cost functions of the LMF algorithm with different sparse penalties; (2) derive their lower bounds; and (3) provide experiment results to show the performance advantage of the propose method in low SNR region. Copyright © 2013 John Wiley & Sons, Ltd.     

复合高斯杂波中自适应目标检测算法

本文研究了复合高斯杂波中已知给定距离-多普勒单元多普勒频率、未知目标幅度的自适应检测目标的问题。杂波散斑分量用未知自回归系数的自回归过程建模表示,通过求解自回归系数和目标幅度的最大似然估计,推导得到复合高斯杂波中的广义似然比检测器。在目标信号向量维数很大的情况下,该检测器对激励信号方差与纹理分量的乘积具有恒虚警率特性。数值仿真表明该检测器性能优于正则化自适应匹配滤波器。最后分析了目标信号向量维数、自回归模型阶数在不同信杂比下对检测器性能的影响。      

Adaptive Detection of Signals in Impulsive Noise Environments

A detector structure and an adaptive algorithm are proposed for the reception of signals in noise backgrounds possessing broad-tailed probability distributions typical of impulsive noise. The adaptive detector combines the best features of linear matched filtering and hard-limiting receiver structures resulting in a small-signal SNR performance which is an improvement over either of these detectors alone. Furthermore, the adaptive detector is relatively easy to implement and is shown to provide efficient and robust performance for a wide range of underlying noise distributions.     

Adaptive path selective fuzzy decorrelating detector under impulsive noise for multipath fading CDMA systems

This letter proposes a novel adaptive path selective fuzzy decorrelating (APSFD) detector for direct-sequence code- division multiple-access (DS-CDMA), systems with time-varying multipath fading channels under impulsive noise. The proposed detector combines adaptive path selective decorrelating (APSD) detector with a fuzzy median filter which is based on fuzzy rank ordering of samples to eliminate the effect of impulsive noise. Simulation results clearly show that the proposed detector eliminates the effect of impulsive noise and improves the performance of APSD detector under high impulsive noise.     

Effect of mismatched SNR on the performance of log-MAP turbo detector

The effect of signal-to-noise ratio (SNR) mismatch on the bit error rate (BER) performance of log-MAP turbo detector is studied. It is shown that the sensitivity to the mismatched SNR depends on the channel loss and the encoder memory length. In particular, the sensitivity is more important for a "difficult-to-equalize" channel or, in other words, a high-loss channel. In fact, by affecting the equalizer and the decoder, a mismatched SNR (with an offset with respect to the true SNR) affects the convergence of the turbo detector. Using the asymptotic analysis tool of extrinsic information transfer (EXIT) charts, the effect of a positive or a negative SNR offset on the convergence of the log-MAP turbo detector is studied. When no information is available at the receiver on SNR, an online estimation of SNR is necessary in order to not lose the advantage of the log-MAP turbo detector over other suboptimal turbo detectors. In this view, a relatively simple SNR estimation method is proposed, which offers satisfying results.     

Group-blind multiuser detection for uplink CDMA

Previously developed blind techniques for multiuser detection in code division multiple access (CDMA) systems lead to several near-far resistant adaptive receivers for demodulating a given user's data with the prior knowledge of only the spreading sequence of that user. In the CDMA uplink, however, typically the base station receiver has the knowledge of the spreading sequences of all the users within the cell, but not that of the users from other cells. In this paper, group-blind techniques are developed for multiuser detection in such scenarios. These new techniques make use of the spreading sequences and the estimated multipath channels of all known users to suppress the intracell interference, while blindly suppressing the intercell interference. Several forms of group-blind linear detectors are developed based on different criteria. Moreover, group-blind multiuser detection in the presence of correlated noise is also considered. In this case, two receiving antennas are needed for channel estimation and signal separation. Simulation results demonstrate that the proposed group-blind linear multiuser detection techniques offer substantial performance gains over the blind linear multiuser detection methods in a CDMA uplink environment     

Non-Coherent Fourth-Order Detector for Impulse Radio Ultra Wideband Systems: Empirical Evaluation Using Channel Measurements

Low-complex and low-power non-coherent energy detectors (EDs) are interesting for low data rate impulse radio (IR) ultra wideband (UWB) systems, but suffer from a loss in performance compared to coherent receivers. The performance of an ED also strongly depends on the integration interval (window size) of the integrator and the window position. This paper presents a non-coherent fourth-order detector (FD) which can discriminate between Gaussian noise signals and non-Gaussian IR-UWB signals by directly estimating the fourth-order moment of the received signal. The performance of the detectors is evaluated using realistic channels measured in a corridor, an office and a laboratory environment. The results show that bit-error-rate (BER) performance of the proposed FD receiver is slightly better than the ED in low signal-to-noise ratio (SNR) region and its performance improves as the SNR increases. In addition, BER of the FD receiver is less sensitive to overestimation of the integration interval making it relatively robust to variations of the channel delay spread. Finally, a criteria for the selection of integration time of the proposed detector is suggested.     

Adaptive array detection of uncertain rank one waveforms

Adaptive array detection of known (within a complex scaling) rank one space time waveforms in unknown spatial noise has received considerable attention. The two published solutions are the adaptive matched filter, and the GLRT. We expand on this work to consider the case of rank one waveforms that are uncertain, i.e., only partially known. More precisely, we model the space time steering vector as the Kronecker product of two vectors, each of which is unknown but is known to lie in a known subspace. Applications for such a model include detection in the presence of multipath and spectral or polarization diversity in both radar processing and wireless communication. Using the principle of invariance, we construct detectors based on the maximal invariant. We show that the SNR required to achieve a given detection probability (for a given false alarm rate) is only weakly impacted by waveform uncertainty. Thus, our detector approaches the performance of earlier detectors, which entail known waveforms     

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 ( ${\varepsilon}$ ).     

基于相位匹配原理的稳健方位估计

文中介绍了一种新的信号方位估计原理--相位匹配原理(PMT),该原理计算简单、且不受噪声相关特性的影响。论文提出了一个新的基于矩阵范数的判别准则,该准则极大地改进了方位的估计精度和方差,并保持了算法计算简单的特点。仿真结果表明改进后的准则在高信噪比下接近MUSIC的估计性能,在低信噪比下却超过MUSIC估计,而且其估计性能不受未知相关噪声的影响。     

Adaptive Channel Estimation Using Pilot-Embedded Data-Bearing Approach for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems employing coherent receivers crucially require channel state information (CSI). Since the multipath delay profile of channels is arbitrary in the MIMO-OFDM systems, an effective channel estimator is needed. In this paper, we first develop a pilot-embedded data-bearing (PEDB) approach for joint channel estimation and data detection, in which PEDB least-square (LS) channel estimator and maximum-likelihood (ML) data detection are employed. Then, we propose an LS fast Fourier transform (FFT)-based channel estimator by employing the concept of FFT-based channel estimation to improve the PEDB-LS one via choosing a certain number of significant taps for constructing a channel frequency response. The effects of model mismatch error inherent in the proposed LS FFT-based estimator when considering noninteger multipath delay profiles and its performance analysis are investigated. The relationship between the mean-squared error (MSE) and the number of chosen significant taps is revealed, and hence, the optimal criterion for obtaining the optimum number of significant taps is explored. Under the framework of pilot embedding, we further propose an adaptive LS FFT-based channel estimator employing the optimum number of significant taps to compensate the model mismatch error as well as minimize the corresponding noise effect. Simulation results reveal that the adaptive LS FFT-based estimator is superior to the LS FFT-based and PEDB-LS estimators under quasi-static channels or low Doppler's shift regimes     

基于改进互耦系数估计算法的自适应波束形成

阵列信号在实际应用中,常常会面临噪声的干扰,低信噪比(signal noise ratio, SNR)时波束形成器性能会急剧下降.针对该问题,提出一种基于改进互耦系数估计的抗互耦算法的自适应波束形成,即研究信号在不同输入SNR以及不同干扰波达方向(direction of arrival,DOA)估计误差下的互耦系数估计误差;并通过误差分析修正原算法不同SNR下参与估计信号的数目.改进算法改进了导向矢量和期望信号,提高了低SNR下互耦系数估计的准确度,对比分析验证了其在低SNR情况下有更强的抗干扰能力,对互耦现象有着更好的稳健性.     

Performance of Belief Propagation for Decoding LDPC Codes in the Presence of Channel Estimation Error

In this paper, we investigate the performance of the belief propagation (BP) algorithm for decoding low-density parity-check codes over the additive white Gaussian noise channel when there is an incorrect estimate of the channel signal-to-noise ratio (SNR) (referred to as "SNR mismatch") at the decoder. At the extremes for over- and underestimation of SNR, the performance of BP tends to that of min-sum algorithm and the channel bit-error rate, respectively. Our results for regular codes indicate that the sensitivity to mismatch increases by increasing the variable-node degree and by decreasing the check-node degree. The effect of variable-node degree, however, appears to be more profound, such that at a given rate, the codes with the smallest variable and check degrees are more robust against SNR mismatch. For irregular codes, by comparing the thresholds of a few ensembles, we demonstrate that the ensemble which performs better in the absence of mismatch can perform worse in the presence of it. To obtain our asymptotic results, we propose a computationally efficient method based on the Gaussian approximation of density evolution in the presence of SNR mismatch. We also show that the asymptotic results are consistent with simulation results for codes with finite block lengths     

Feature classification criterion for missing features mask estimation in robust speaker recognition

Currently, many speaker recognition applications must handle speech corrupted by environmental additive noise without having a priori knowledge about the characteristics of noise. Some previous works in speaker recognition have used the missing feature (MF) approach to compensate for noise. In most of those applications, the spectral reliability decision step is performed using the signal to noise ratio (SNR) criterion, which attempts to directly measure the relative signal to noise energy at each frequency. An alternative approach to spectral data reliability has been used with some success in the MF approach to speech recognition. Here, we compare the use of this new criterion with the SNR criterion for MF mask estimation in speaker recognition. The new reliability decision is based on the extraction and analysis of several spectro-temporal features from across the entire speech frame, but not across the time, which highlight the differences between spectral regions dominated by speech and by noise. We call it the feature classification (FC) criterion. It uses several spectral features to establish spectrogram reliability unlike SNR criterion that relies only in one feature: SNR. We evaluated our proposal through speaker verification experiments, in Ahumada speech database corrupted by different types of noise at various SNR levels. Experiments demonstrated that the FC criterion achieves considerably better recognition accuracy than the SNR criterion in the speaker verification tasks tested.     

Design of Irregular LDPC Codes for BIAWGN Channels with SNR Mismatch

Belief propagation (BP) algorithm for decoding lowdensity parity-check (LDPC) codes over a binary input additive white Gaussian noise (BIAWGN) channel requires the knowledge of the signal-to-noise ratio (SNR) at the receiver to achieve its ultimate performance. An erroneous estimation or the absence of a perfect knowledge of the SNR at the decoder is referred to as ?SNR mismatch?. SNR mismatch can significantly degrade the performance of LDPC codes decoded by the BP algorithm. In this paper, using extrinsic information transfer (EXIT) charts, we design irregular LDPC codes that perform better (have a lower SNR threshold) in the presence of mismatch compared to the conventionally designed irregular LDPC codes that are optimized for zero mismatch. Considering that min-sum (MS) algorithm is the limit of BP with infinite SNR over-estimation, the EXIT functions generated in this work can also be used for the efficient analysis and design of LDPC codes under the MS algorithm.