Robust detection with the gap metric

In a multipath communication channel, the optimal receiver is matched to the maximum likelihood (ML) estimate of the multipath signal. In general, this leads to a computationally intensive multidimensional nonlinear optimization problem that is not feasible in most applications. We develop a detection algorithm that avoids finding the ML estimates of the channel parameters while still achieving good performance. Our approach is based on a geometric interpretation of the multipath detection problem. The ML estimate of the multipath signal is the orthogonal projection of the received signal on a suitable signal subspace S. We design a second subspace G, which is the representation subspace, that is close to S but whose orthogonal projection is easily computed. The closeness is measured by the gap metric. The subspace G is designed by using wavelet analysis tools coupled with a reshaping algorithm in the Zak transform domain. We show examples where our approach significantly outperforms the conventional correlator receiver (CR) and other alternative suboptimal detectors     

Time-frequency projection filters and time-frequency signalexpansions

We consider the problems of designing a linear, time-varying filter with a specified “time-frequency (TF) pass region” and of constructing an orthonormal basis for the parsimonious expansion of signals located in a given TF support region. These problems of TF filtering and TF signal expansion are reduced to the problem of designing a “TF subspace”, i.e., a linear signal space comprising all signals located in a given TF legion. Specifically, the TF filter is taken to be the orthogonal projection operator on the TF subspace. We present an optimum design of TF subspaces that is based on the Wigner distribution of a linear signal space and is an extension of the well-known signal synthesis problem. The optimum TF subspace is shown to be an “eigenspace” of the TF region, and some properties of eigenspaces are discussed. The performance of TF projection filters and TF signal expansions is studied both analytically and via computer simulation     

Blind multiuser detector based on orthogonal projection [for CDMA]

A new stable blind multiuser detector based on signal subspace orthogonal projection is presented. By carrying out a pseudoeigendecomposition (PED) analysis, a technique proposed for the first time, the signal of the interested user can be approximated by the orthogonal projection of a received multiuser signal onto the related subspace of the interested user. Simulations show that compared with the previous subspace-based MMSE blind multiuser detector, the new detector offers better performance without leading to an increase in the computational complexity     

A Subspace Projection Approach for Analysis of Speech Under Stressed Condition

In this paper, a novel subspace projection approach is proposed for analysis of speech signal under stressed condition. The subspace projection method is based on the assumption of orthogonality between speech subspace and stress subspace. Speech and stress subspaces contain speech and stress information, respectively. The projection of stressed speech vectors onto the speech subspace will separate speech-specific information. In this work, the speech subspace consists of neutral speech vectors. Speech and stress recognition techniques are used to verify the orthogonal relation between speech and stress subspaces. The evaluation database consists of 119 word vocabulary under neutral, angry, sad and Lombard conditions. Hidden Markov models for speech and stress recognition are used with mel-frequency cepstral coefficient features for evaluation of estimated speech and stress information.     

Blind space-time multiuser channel estimation in time-varying DS-CDMA systems

A multistep linear prediction (MSLP) approach is presented for blind channel estimation for short-code direct sequence code division multiple access signals in time-varying multipath channels using a receiver antenna array. The time-varying channel is assumed to be described by a complex exponential basis expansion model. First, a recently proposed MSLP approach to blind channel estimation for time-varying single-input multiple-output (SIMO) systems is extended to time-varying multiple-input multiple-output (MIMO) systems to define a "signal" subspace. Second, the knowledge of the spreading code of a desired user is exploited in conjunction with the signal subspace to estimate the time-varying channel of the desired user up to an unknown time-invariant scale factor. Equalization/detection for the desired user can be then carried out if the information sequence is differentially encoded/decoded. Sufficient conditions for channel identifiability are investigated. Three illustrative simulation examples are provided.     

Optimum Receiver Design for Broadband Doppler Compensation in Multipath/Doppler Channels With Rational Orthogonal Wavelet Signaling

In this paper, we address the issue of signal transmission and Doppler compensation in multipath/Doppler channels. Based on a wavelet-based broadband Doppler compensation structure, this paper presents the design and performance characterization of optimum receivers for this class of communication systems. The wavelet-based Doppler compensation structure takes account of the coexistence of multiple Doppler scales in a multipath/Doppler channel and captures the information carried by multiple scaled replicas of the transmitted signal rather than an estimation of an average Doppler as in conventional Doppler compensation schemes. The transmitted signal is recovered by the perfect reconstruction (PR) wavelet analysis filter bank (FB). We demonstrate that with rational orthogonal wavelet signaling, the proposed communication structure corresponds to a Lth-order diversity system, where L is the number of dominant transmission paths. Two receiver designs for pulse amplitude modulation (PAM) signal transmission are presented. Both receiver designs are optimal under the maximum-likelihood (ML) criterion for diversity combination and symbol detection. Good performance is achieved for both receivers in combating the Doppler effect and intersymbol interference (ISI) caused by multipath while mitigating the channel noise. In particular, the second receiver design overcomes symbol timing sensitivities present in the first design at reasonable cost to performance.     

多速率CDMA系统的盲自适应多用户接收机

该文研究可变扩频长度(Variable Spreading Length, VSL) 多速率直接序列码分多址(Direct-Sequence Code-Division Multiple-Access, DS-CDMA) 系统中的自适应多用户接收机。利用到相交子空间的迭代投影估计得到包含信道信息的特征矢量,以此构造多用户接收机。借助于子空间跟踪算法的特殊形式,推导了特征矢量估计算法及多用户接收机的一种低复杂度的自适应实现。仿真实验证明,该算法有很好的数值稳定性,与传统算法BER性能相同的情况下,具有更低的计算复杂度。     

Multislot estimation of fast-varying space-time channels in TD-CDMA systems

We propose a subspace method to estimate the time-varying space-time channel by taking into account the different rates of variation between delays/directions of arrival (slowly-varying) and faded amplitudes (fast-varying) of the multipath propagation. The stationarity of angles and delays across several time-slots is exploited to perform an unstructured estimation that avoids the explicit computation of the path parameters. Numerical analysis for a TD-CDMA system shows that the asymptotic performance is reached with a reasonable number of slots.     

Blind adaptive reduced-rank detection for DS-CDMA signals inmultipath channels

Blind adaptive multiuser detection for direct sequence code division multiple access (DS-CDMA) signals over static and time-varying intersymbol interference (ISI) limited channels is considered. Blind adaptive detectors must be robustified for ISI channels, when there is significant mismatch between the received signature vector and the transmitted code (assumed known at the receiver). A new low-complexity detector is presented that improves on some previously proposed methods without explicit estimation of the ISI channel. The key innovation is a reduced-rank detector architecture combined with an efficient subspace tracker that yields direct accurate estimation of the desired user's received signature. Several representative simulation examples of detector output signal-to-noise-and-interference ratio (SINR) for fading channels are provided in support of our claims of improved efficacy of the method     

Subspace-based error and erasure correction with DFT codes for wireless channels

This paper deals with the decoding of lowpass discrete Fourier transform (DFT) codes in the presence of both errors and erasures. We propose a subspace-based approach for the error localization that is similar to the subspace approaches followed in the array signal processing for direction-of-arrival (DOA) estimation. The basic idea is to divide a vector space into two orthogonal subspaces of which one is spanned by the error locator vectors. The locations of the errors are estimated from the spanning eigenvectors of the complement subspace. However, unlike the subspace approach in DOA estimation, which is similar to estimating the subspaces from the syndrome covariance matrix after a projection, in the proposed approach, the subspaces are estimated from the modified syndrome covariance matrix after a whitening transform. Simulation results with a Gauss-Markov source reveal that the proposed algorithm is more efficient than the coding theoretic approach on impulsive channels as well as the subspace approach with projection on lossy channels.     

Subspace Projection-based OFDM Channel Estimation

In this paper, we investigate the benefits of pre-processing received data by projection on the performance of channel estimation for orthogonal frequency division multiplexing (OFDM) systems. Projecting data onto its signal subspace will reduce the additive noise energy in the data. Least square (LS) estimation is a low-complex algorithm for training-based OFDM systems and the lower bound on the mean-square error of it is proportional to the noise variance. So, after the received data is pre-processed (projected onto its signal subspace), LS channel estimation on the pre-processed data will increase the performance of channel estimation. This method can also work in multiple-input and multiple-output (MIMO) case. Performance analysis and simulation results show that the proposed algorithm has a considerably smaller complexity than the linear minimum mean square error estimation while having almost the same performance.     

A Systematic Approach to Multistage Detectors in Multipath Fading Channels

We consider linear multistage detectors with universal (large system) weighting for synchronous code-division multiple access (CDMA) in multipath fading channels with many users. A convenient choice of the basis of the projection subspace allows a joint projection of all users. Taking advantage of this property, the complexity per bit of multistage detectors with universal weights scales linearly with the number of users on the uplink CDMA channel, while other known multistage detectors with universal weights and different bases of the projection subspace keep the same quadratic complexity order per bit as the linear minimum mean-square error (LMMSE) detector. We focus on the design of two kinds of detectors with linear complexity. The detector of Type I is obtained as an asymptotic approximation of the polynomial expansion detector proposed by Moshavi The detector of Type II has the same performance as the multistage Wiener filter (MSWF) in large systems. Additionally, general performance expressions for large systems, applicable to any multistage detector with the same basis of the projection subspace (e.g., linear parallel interference canceling detectors), are derived. As a by-product, the performance analysis disproves the widespread belief that the MSWF and the polynomial expansion detector are equivalent. We show that, in general, the MSWF outperforms the latter one and they are equivalent only asymptotically in the case of equal received powers.     

Lesion Detection in Dynamic FDG-PET Using Matched Subspace Detection

We describe a matched subspace detection algorithm to assist in the detection of small tumors in dynamic positron emission tomography (PET) images. The algorithm is designed to differentiate tumors from background using the time activity curves (TACs) that characterize the uptake of PET tracers. TACs are modeled using linear subspaces with additive Gaussian noise. Using TACs from a primary tumor region of interest (ROI) and one or more background ROIs, each identified by a human observer, two linear subspaces are identified. Applying a matched subspace detector to these identified subspaces on a voxel-by-voxel basis throughout the dynamic image produces a test statistic at each voxel which on thresholding indicates potential locations of secondary or metastatic tumors. The detector is derived for three cases: using a single TAC with white noise of unknown variance, using a single TAC with known noise covariance, and detection using multiple TACs within a small ROI with known noise covariance. The noise covariance is estimated for the reconstructed image from the observed sinogram data. To evaluate the proposed method, a simulation-based receiver operating characteristic (ROC) study for dynamic PET tumor detection is designed. The detector uses a dynamic sequence of frame-by-frame 2-D reconstructions as input. We compare the performance of the subspace detectors with that of a Hotelling observer applied to a single frame image and of the Patlak method applied to the dynamic data. We also show examples of the application of each detection approach to clinical PET data from a breast cancer patient with metastatic disease.      

Optimal time-frequency deconvolution filter design fornonstationary signal transmission through a fading channel: AF filterbank approach

The purpose of this paper is to develop a new approach-time-frequency deconvolution filter-to optimally reconstruct the nonstationary (or time-varying) signals that are transmitted through a multipath fading and noisy channel. A deconvolution filter based on an ambiguity function (AF) filter bank is proposed to solve this problem via a three-stage filter bank. First, the signal is transformed via an AF analysis filter bank so that the nonstationary (or time-varying) component is removed from each subband of the signal. Then, a Wiener filter bank is developed to remove the effect of channel fading and noise to obtain the optimal estimation of the ambiguity function of the transmitted signal in the time-frequency domain. Finally, the estimated ambiguity function of the transmitted signal in each subband is sent through an AF synthesis filter bank to reconstruct the transmitted signal. In this study, the channel noise may be time-varying or nonstationary. Therefore, the optimal separation problem of multicomponent nonstationary signals is also solved by neglecting the transmission channel     

Spatial-Doppler domain precoding for orthogonal time frequency space modulation with rake detector

The orthogonal time frequency space (OTFS) transmission scheme was shown to outperform orthogonal frequency division multiplexing (OFDM) under the doubly dispersive channel. In this paper, the linear precoding is studied for multiple-input and multiple-out (MIMO) OTFS systems, in which a spatial-Doppler domain singular value decomposition (SVD) precoding scheme is proposed. At the transmitter, the Doppler domain symbols from different spatial streams are precoded before projected onto multiple antennas for transmission. At the receiver, multipath components of the transmitted symbols in the delay-Doppler grid are combined by using maximal ratio combining (MRC) strategy, so as to achieve the multipath diversity gain and increase the reception reliability. The achievable rate and complexity of the proposed scheme are analyzed, revealing that it can increase the achievable rate while reducing the detector complexity as well. The simulation results confirm that the SVD-based precoding significantly enhances the error performance of the MIMO-OTFS with MRC-based detector.     

Iterative Joint Channel Estimation and Signal Detection for OFDM System in Double Selective Channels

Intercarrier interference caused by fast time-varying multipath fading channels degrades the system performance of high-mobility orthogonal frequency division multiplexing systems. This study considers the challenging problem of joint channel estimation and signal detection in high mobility environments. The estimation method is based on a pilot-aided linear approximation channel modeling and iterative process. After each iteration, the channel estimates are refined with the fed-back detection signal. The channel is re-estimated iteratively, detected increasingly reliable signals. The proposed method is independent of the Doppler-spectrum, delay-profile shape and the number of paths. Numerical simulation results indicate that the proposed method is highly robust to fast time-varying multipath fading channels.     

DRM外辐射源雷达多普勒扩展杂波抑制算法研究

数字调幅广播(DRM)外辐射源雷达面临严重的多径杂波问题,其中不仅包含静止地面散射物引起的无多普勒频移多径杂波,还有经运动海面等散射引起的具有多普勒频移扩展的多径杂波。扩展相消杂波抑制类(ECA)算法常被用于无源雷达杂波抑制,但均仅针对无多普勒频移的多径杂波。文中建立了考虑多普勒频移扩展杂波的该无源雷达信号模型,结合信号波形特点提出了载波域ECA算法(ECA-C)的扩展算法,深入分析了扩展ECA-C算法的多径杂波抑制机理。该方法通过对各载波信号组成进行分析,构造相应的杂波子空间,利用子空间正交投影可同时对静止的和具有多普勒频移的多径杂波进行抑制,提高了系统的杂波抑制能力,进而改善了弱目标检测能力。理论分析和实测数据处理结果表明了该算法的优越性。     

Subspace averaging of steady-state visual evoked potentials

A new algorithm for doing signal averaging of steady-state visual evoked potentials (VEP's) is described. The subspace average is obtained by finding the orthogonal projection of the VEP measurement vector onto the signal subspace, which is based on a sinusoidal VEP signal model. The subspace average is seen to out-perform the conventional average using a new signal-to-noise-ratio-based performance measure on simulated and actual VEP data.     

Adaptive MMSE maximum likelihood CDMA multiuser detection

The well-known code division multiple access maximum likelihood receiver (MF-ML) uses a bank of matched filters as a generator of sufficient statistics for maximum likelihood detection of users transmitted symbols. In this paper, the bank of matched filters is replaced by a bank of adaptive minimum mean squared error (MMSE) filters as the generator of sufficient statistics. This formal replacement of the MF bank by the adaptive MMSE filter bank has significant conceptual consequences and provides improvement by several performance measures. The adaptive MMSE-ML receiver's digital implementation is significantly computationally simplified. The advantages of the proposed adaptive MMSE-ML receiver over the MF-ML receiver are: (1) ability to perform joint synchronization, channel parameter estimation, and signal detection where the signal is sent over an unknown, slowly time-varying, frequency-selective multipath fading channel; (2) increased information capacity in a multicellular environment; and (3) significantly improved bit error rate (BER) performance in a multicellular mobile communications environment. The information capacity and the BER of the proposed MMSE-ML receiver are analyzed. Numerical results showing the BER performance of the MMSE-ML receiver in a multipath channel environment are presented     

Channel estimation for OFDM transmission in multipath fadingchannels based on parametric channel modeling

We present an improved channel estimation algorithm for orthogonal frequency-division multiplexing mobile communication systems using pilot subcarriers. This algorithm is based on a parametric channel model where the channel frequency response is estimated using an L-path channel model. In the algorithm, we employ the ESPRIT (estimation of signal parameters by rotational invariance techniques) method to do the initial multipath time delays acquisition and propose an interpath interference cancellation delay locked loop to track the channel multipath time delays. With the multipath time delays information, a minimum mean square error estimator is derived to estimate the channel frequency response. It is demonstrated that the use of the parametric channel model can effectively reduce the signal subspace dimension of the channel correlation matrix for the sparse multipath fading channels and, consequently, improve the channel estimation performance