Blind Channel Estimation Research Articles

Generalized Maximum Likelihood for Cross-Polarization Modulation Effects Compensation

We investigate the mitigation of nonlinearities with advanced digital signal processing focusing in particular on cross-polarization effects. Based on a relaxation of an analytical model derived for cross-polarization effects, this paper proposes a novel compensation method called generalized maximum likelihood. It performs a joint blind channel estimation and symbol detection, and it additionally accounts for the statistical prior distributions of the cross-polarization crosstalk coefficients. This avoids an overestimation of these crosstalk coefficients. A practical method for both fast computations and optimal performance is then presented, which allows nonlinear compensation for high-order modulations. Next, we present Monte-Carlo simulations showing that the proposed algorithm performs close to the theoretical limits. Large performance improvement can be obtained and this is particularly emphasized with higher order modulation such as a 16-ary quadrature amplitude modulation. Finally, using Nyquist pulse shaping and polarization-division multiplexed with a quadrature phase-shift keying modulation, the experiments are shown to be in accordance with the simulations and show up to 0.7 dB improvement in Q-factor for the worst-case samples.

Space-Time Diversity Schemes with Blind Channel Estimation for TH-PPM UWB MIMO Communications

—For a time-hopping (TH) pulse position modulation (PPM) based ultra-wideband (UWB) multiantenna system, we propose four coherent space-time (ST) diversity schemes, corresponding to which the correlation template signals with blind channel estimation are also presented. First of all, the issue of channel identifiability based on first-order statistics is addressed, aspired by the singleantenna case [1]. Then, we shed light on that the determination of space-time structure is related to both channel estimation and signal detection. After that, four ST diversity structures are presented to guarantee both the feasibility of blind channel estimation and no multi-antenna interference introduced into signal detection. Finally, substantial simulations show that the proposed ST diversity schemes can significantly outperform the traditional single-antenna system especially for high signal-tonoise ratios whether channel state information is perfect or not.

An investigation into performance of MIMO wireless communication system under semi-blind channel estimation

A new approach to Semi–Blind Channel Estimation (SBCE) technique for Multiple Input Multiple Output (MIMO) wireless communication system is proposed in this paper. It combines the least squares (LS) and Minimum Mean Square Error (MMSE) Training–Based Channel Estimation (TBCE) scheme with whitening rotation–based SBCE scheme. In this approach, the singular value decomposition has to be applied only once, thereby reducing the computational complexity. The channel correlation matrix required by MMSE technique can be obtained based on latest channel conditions. The performance of this scheme is compared with whitening rotation–based SBCE under various parameters such as modulation schemes, Signal–to–Noise Ratio (SNR) and frame length. The performance of the new approach is found to be better in terms of Bit Error Rate (BER) for high SNR, higher modulation order and smaller frame lengths. With low SNR, lower modulation order and larger frame lengths, there is negligible reduction in performance.

Novel Fast Blind Channel Estimation and Hessian Analysis for Transmitter Identification of Digital Television Signals

The transmitter identification (Tx-ID) technique has been widely addressed in the modern advanced television systems committee digital television (DTV) standards. Kasami sequences are adopted as the Tx-ID sequences due to the favorable code capacity. However, the conventional cross-correlation based Tx-ID technique cannot combat the realistic multipath channel impairment. Therefore, in this paper, we propose a new least-square Tx-ID approach, which is demonstrated to be very robust when the multipath channels are encountered. We also establish the new Hessian analysis to provide the range for appropriate injection levels accordingly. Based on this new theoretical study, we design new efficient (fast) algorithms to determine the maximum allowable channel length given a particular Kasami Tx-ID sequence. These algorithms and theoretical results can be very useful for the future DTV technologies.

A New Precoder Design for Blind Channel Estimation in MIMO-OFDM Systems

A new precoder for precoding-based blind channel estimation for MIMO-OFDM systems is proposed. In the proposed scheme, only a small number of data symbols, commensurate with the channel length, are linearly precoded prior to transmission to induce the signal correlation needed for the scheme to blindly estimate the channels. Similar to how pilot symbols are transmitted, the subcarriers carrying these linearly precoded data symbols are equi-spaced across the frequency band. The other subcarriers carry data symbols in the standard way, enabling MLD per subcarrier and also allowing for MIMO linear precoding across antennas at each subcarrier. This is in contrast to previous precoding-based blind channel estimation schemes which precode all of the data symbols so that every subcarrier carries a linear combination of symbols, aking the resulting joint MLD problem infeasible. In addition, this also makes it infeasible to employ MIMO precoding per subcarrier across the transmit antennas. The proposed precoder is designed via a multi-stage optimization process that seeks to minimize both channel estimation error and symbol estimation error. For channel estimation purposes, the resulting optimal design offers low-cost features such as sign change and FFT while providing reasonable channel estimation performance for low mobility applications.

Obtaining underwater acoustic impulse responses via blind channel estimation

Blind channel estimation is the process of obtaining the impulse responses between a source and multiple (arbitrarily placed) receivers without prior knowledge about the source characteristics or the environment. This approach could simplify localization of non-impulsive submerged sound sources (e.g., pinnipeds or cetaceans); the process of picking arrivals (direct and reflected) could be carried out on the estimated impulse responses rather than on the recorded waveforms, thus facilitating the use of time of arrival-based localization approaches. Blind channel estimation could also be useful in estimating the original source signal of a vocalizing animal through deconvolution of the estimated channel impulse responses and the recorded waveforms. In this paper, simulation and controlled pool studies will be used to explore requirements on source and environment characteristics and to quantify blind channel estimation performance for underwater passive acoustic applications.

Blind channel estimation for multiple antenna OFDM system subject to unknown carrier frequency offset

The problem of channel estimation for multiple antenna orthogonal frequency division multiplexing (OFDM) systems subject to unknown carrier frequency offset (CFO) is addressed. Multiple signal classification (MUSIC)-like algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional (RD)-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cramér-Rao bound (CRB) of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.

Blind Channel and Source Estimation in Networked Systems

In this paper, we study the blind channel and source estimation in sensor networks, where the channels are modeled by FIR filters and the source signal is deterministic. Distributed estimation algorithms for networked systems under noise-free and noisy measurements are developed, which blindly identify the multiple channels, followed by the source signal estimation. The key to the proposed algorithms lies in the adaptation of the blind system identification technique for the distributed channel estimation. In the presence of measurement noises, conventional blind identification methods cannot be straightforwardly realized in distributed environments. Instead, two stable distributed algorithms are introduced, which can avoid trivial solutions for the blind identification problem. Convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

One-Shot Blind CFO and Channel Estimation for OFDM With Multi-Antenna Receiver

In this paper, we design a new blind joint carrier frequency offset (CFO) and channel estimation method for orthogonal frequency-division multiplexing (OFDM) with multiantenna receiver. The proposed algorithm requires only one received OFDM block and thus belongs to the category of one-shot estimation methods. Other advantages of the proposed algorithm include 1) it supports fully loaded data carriers and is thus spectral efficient; 2) the channel from the transmitter to each receive antenna can be blindly estimated with only a scaling ambiguity; and 3) the algorithms outperforms the existing methods. Moreover, we derive the Cramer-Rao Bounds (CRB) of joint CFO and channel estimation in closed forms. Numeral results not only show the effectiveness of the proposed algorithm but also demonstrate its closed performance to CRB.

Multichannel myopic deconvolution using ambient noise sources

The ocean ambient noise has been increasingly utilized for ocean passive sensing and monitoring applications. By recording the received signals from the same individual noise source (for example, the shipping noise) at multiple hydrophones simultaneously, we develop a framework, called multichannel myopic deconvolution, which can allow us jointly estimate the source and the channel responses without any assumption about the source, but using some priori knowledge of the channel. Our work on this classical signal processing problem has two novel aspects. First, we recast the corresponding bilinear system of equations as a linear system with a rank constraint. This allows us to apply recently developed algorithms and analytical tools from the field of low-rank recovery to the blind channel estimation problem, yielding insight into the conditions under which accurate channel estimation is possible. Second, we incorporate (continuous-time) parametric uncertainty about the Green's functions as subspace constraints in the low-rank recovery problem. These subspaces are generated in a systematic way using the singular value decomposition, and their dimension can be directly related to the amount of priori knowledge we have about the channel. We will present simulations in shallow water environments of the proposed approach from relatively short observation times.

Blind Deconvolution Using Convex Programming

We consider the problem of recovering two unknown vectors, w and x, of length L from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension N and the other with dimension K. Although the observed convolution is nonlinear in both w and x, it is linear in the rank-1 matrix formed by their outer product wx*. This observation allows us to recast the deconvolution problem as low-rank matrix recovery problem from linear measurements, whose natural convex relaxation is a nuclear norm minimization program. We prove the effectiveness of this relaxation by showing that, for “generic” signals, the program can deconvolve w and x exactly when the maximum of N and K is almost on the order of L. That is, we show that if x is drawn from a random subspace of dimension N, and w is a vector in a subspace of dimension K whose basis vectors are spread out in the frequency domain, then nuclear norm minimization recovers wx* without error. We discuss this result in the context of blind channel estimation in communications. If we have a message of length N, which we code using a random L x N coding matrix, and the encoded message travels through an unknown linear time-invariant channel of maximum length K, then the receiver can recover both the channel response and the message when L ≳ N + K, to within constant and log factors.

Blind Channel Estimation Based on Multilevel Lloyd-Max Iteration for Nonconstant Modulus Constellations

In wireless communications, knowledge of channel coefficients is required for coherent demodulation. Lloyd-Max iteration is an innovative blind channel estimation method for narrowband fading channels. In this paper, it is proved that blind channel estimation based on single-level Lloyd-Max (SL-LM) iteration is not reliable for nonconstant modulus constellations (NMC). Then, we introduce multilevel Lloyd-Max (ML-LM) iteration to solve this problem. Firstly, by dividing NMC into subsets, Lloyd-Max iteration is used in multilevel. Then, the estimation information is transmitted from one level to another. By doing this, accurate blind channel estimation for NMC is achieved. Moreover, when the number of received symbols is small, we propose the lacking constellations equalization algorithm to reduce the influence of lacking constellations. Finally, phase ambiguity of ML-LM iteration is also investigated in the paper. ML-LM iteration can be more robust to the phase of fading coefficient by dividing NMC into subsets properly. As the signal-to-noise ratio (SNR) increases, numerical results show that the proposed method’s mean-square error curve converges remarkably to the least squares (LS) bound with a small number of iterations.

RFID Reader Receiver Using Blind Signal Estimation for Multiuser Detection

 Abstract—Radio frequency identification (RFID) is a wireless technology that uses radio backscatter signals to purpose of identify objects. Especially, in the microwave frequency as 2.45 GHz is growing rapidly. Since the physical environment surrounding the propagation path of radio identification is generally difficult to control, the channel characteristics in backscattering are often channel fading. There are two major sources of impairment of the RFID reader is unreadable as the tag signal propagate through channel. Some works provides to use the multiple antennas for both transmit and receive of RFID readers. In this paper, we propose a perspective of blind channel estimation is presented to the RFID reader receiver for multiuser detection. Furthermore, the multiple-input and multiple-output (MIMO) of channel measurement is verified with the multiple antennas at the RFID reader and provides the multiple tags are under test. We also propose a linear receiver to estimate the received signal, where provides a zero-forcing (ZF) and a minimal mean square error (MMSE) in order to estimate the channel state information (CSI) at the reader receiver. Under the signal estimation, we shown the blind order signal estimated so that the reader can adapt as the multiple tag and available the detection simultaneously.

Fractionally spaced equalizer based on dynamically varying modulus algorithm for spectrally efficient channel compensation in SC-FDMA based systems

Orthogonal frequency division multiplexing (OFDM) based wireless communication systems are expected to satisfy the thirst for ever increasing demand on higher spectral efficiency. But, OFDM systems suffer from peak to average power ratio (PAPR) and inter carrier interference (ICI) problems. It is observed that when OFDM is used in the uplink, PAPR problem is more severe and the relative mobility of the user equipments with respect to the base station will cause Doppler spread which leads to ICI. One of the solutions to minimize PAPR and ICI is single carrier frequency division multiple access. But there is a tradeoff in spectral efficiency. The main objective of this paper is to evaluate the performance of fractionally spaced equalizer (FSE) for blind channel estimation based on higher order statistics and to identify any better alternative to improve its performance. Dynamically varying modulus algorithm (DVMA) based FSE is proposed in this paper which is a better alternative for supervised equalization. The simulation results prove that FSE blind equalizer based on DVMA outperform the conventional supervised and blind equalizers.

Subspace-Based Blind Channel Estimation by Separating Real and Imaginary Symbols for Cyclic-Prefixed Single-Carrier Systems

Blind channel estimation based on a subspace-based algorithm for single-input single-output cyclic-prefixed single-carrier systems is proposed in this brief. The proposed method is different from conventional subspace-based approaches, which exploit the original complex structure of the received data symbols. In contrast, we separate the real part and the imaginary part of the received complex symbols and then exploit these two kinds of symbols to construct the signal model. The noise subspace can then be established to estimate the channel impulse response (CIR) using a subspace algorithm when real symbols, such as binary phase shift keying or pulse amplitude modulation symbols, are applied. The real part and the imaginary part of the CIR can be estimated individually and simultaneously with only a sign ambiguity. With the aid of repetition index, the proposed method is workable even if few data blocks are available. Simulation results demonstrate that the proposed approach outperforms conventional methods in normalized mean-squared error under static channel environments.

Subspace-Based Blind Channel Estimation for OFDM by Exploiting Cyclic Prefix

A new subspace-based blind channel estimation (SBCE) approach for cyclically prefixed orthogonal frequency-division multiplexing (CP-OFDM) is proposed to provide enhanced estimation performance. Based on the redundant structure that CP reproduces the tail part of the useful signal vector, a predetermined null subspace corresponding to the transmitted CP-OFDM signal vector subspace is identified and enables the proposed estimator to extract the channel impluse response by projecting the received signal onto the predetermined null subspace. While requiring less implementation complexity, the proposed channel estimator is shown to outperform conventional SBCE approaches in mean-square error performance when the number of received blocks and the received signal-to-noise power ratio are not large.

Cross‐relation‐based frequency‐domain blind channel estimation with multiple antennas in orthogonal frequency division multiplexing systems

Frequency domain blind channel estimation methods for orthogonal frequency division multiplexing (OFDM) systems with multiple antennas are proposed, in which the cross relation between the channel gains and a single snapshot of the received signal on each subcarrier is utilised. Necessary and sufficient conditions for the identifiability of a single-input multiple-output OFDM channel estimator are derived and, furthermore, using a first-order perturbation analysis, the approximation of mean-squared-error (MSE) is analytically obtained, which shows that the proposed estimator is approximately unbiased at high signal-to-noise ratio. The proposed estimator exhibits lower MSEs than those of both the existing subspace-based and deterministic blind channel estimators and is less sensitive to the estimation error of the number of multipaths. Finally, the extension of the proposed channel estimation to multiple-input-multiple-output OFDM is presented.

On the blind channel identifiability of multiple-input multiple-output space-time block code systems using Joint Approximate Diagonalization of Eigenmatrices

Channel identifiability for multiple-input multiple-output space-time block code MIMO-STBC systems using Joint Approximate Diagonalization of Eigenmatrices JADE is studied in this paper. Compared with the previous blind MIMO-STBC channel estimation methods in literature, the method proposed in this paper is more suitable for non-cooperative scenario because it needs less prior information and can be applied to a general class of STBCs. The main contribution of the paper consists in the theoretical proof that, although the sources transmitted by different antennas of MIMO-STBC systems are not independent, they can be retrieved from the received data by directly using JADE in most cases. The conclusion is also demonstrated by a simulation. This shows that the classical JADE algorithm can be applied to a wider range of situations rather than strictly independent sources. Copyright © 2013 John Wiley & Sons, Ltd.

A Low Complexity Modulation Classification Algorithm for MIMO Systems

A novel algorithm is proposed for automatic modulation classification in multiple-input multiple-output spatial multiplexing systems, which employs fourth-order cumulants of the estimated transmit signal streams as discriminating features and a likelihood ratio test (LRT) for decision making. The asymptotic likelihood function of the estimated feature vector is analytically derived and used with the LRT. Hence, the algorithm can be considered as asymptotically optimal for the employed feature vector when the channel matrix and noise variance are known. Both the case with perfect channel knowledge and the practically more relevant case with blind channel estimation are considered. The results show that the proposed algorithm provides a good classification performance while exhibiting a significantly lower computational complexity when compared with conventional algorithms.

Blind channel estimation for MLSE receiver in high speed optical communications: theory and ASIC implementation

Blind channel estimation is critical for digital signal processing (DSP) compensation of optical fiber communications links. The overall channel consists of deterministic distortions such as chromatic dispersion, as well as random and time varying distortions including polarization mode dispersion and timing jitter. It is critical to obtain robust acquisition and tracking methods for estimating these distortions effects, which, in turn, can be compensated by means of DSP such as Maximum Likelihood Sequence Estimation (MLSE). Here, a novel blind estimation algorithm is developed, accompanied by inclusive mathematical modeling, and followed by extensive set of real time experiments that verify quantitatively its performance and convergence. The developed blind channel estimation is used as the basis of an MLSE receiver. The entire scheme is fully implemented in a 65 nm CMOS Application Specific Integrated Circuit (ASIC). Experimental measurements and results are presented, including Bit Error Rate (BER) measurements, which demonstrate the successful data recovery by the MLSE ASIC under various channel conditions and distances.