Channel Identification Algorithm Research Articles

Channel identification and sequential sequence estimation using antenna array for broad-band mobile communications

This paper proposes the joint use of an antenna array and sequential sequence estimation (SSE) technique for the equalization of multipath fading channels suffering from severe intersymbol interference (ISI). It is shown that with the proper use of the multiple stack algorithm (MSA), exploiting the redundant structure of the signal received by multiple antenna array elements can enhance the uniqueness of the sequence estimation, thereby significantly reducing the frame erasure probability. Three new algorithms are derived for vector channel identification, the results of which are used to calculate the Fano metric for SSE. The first algorithm uses just a temporal reference, and others use both temporal and spatial references. Impacts of using the temporal and spatial references are investigated in terms of the channel identification accuracy as well as overall frame erasure rate (ERR) and bit error rate (BER). Results of computer simulations are then presented to compare the performances of the three algorithms for vector channel identification. The propagation scenario assumed in the simulations is equal-power 12-path propagation, in which, even with binary phase shift keying (BPSK), maximum likelihood sequence estimation (MLSE) requires 2048 states. It is shown that even in such a complicated situation, the MSA algorithm can achieve excellent ERR and BER performances with reasonable computational complexity.

QR factorization based blind channel identification with second-order statistics

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the received signal. In this paper, we address new algorithms based on QR factorization of the received signal directly without calculating the correlation matrix. This renders the QR factorization-based algorithms more robust against ill-conditioned channels, i.e., those channels with almost common zeros among the subchannels. First, we present a block algorithm that performs the QR factorization of the received data matrix as a whole. Then, a recursive algorithm is developed based on the QR factorization by updating a rank-revealing ULV decomposition. Compared with existing algorithms in the same category, our algorithms are computationally more efficient. The computation in each recursion of the recursive algorithm is on the order of O(m/sup 2/) if only equalization is required, where m is the dimension of the received signal vector. Our recursive algorithm preserves the fast convergence property of the subspace algorithms, thus converging faster than other adaptive algorithms such as the super-exponential algorithm with comparable computational complexities. Moreover, our proposed algorithms do not require noise variance estimation. Numerical simulations demonstrate the good performance of the proposed algorithms.

Complexity reduction in subspace-based blind channel identification for DS/CDMA systems

Direct-sequence code-division multiple access is emerging as a potential multiple-access communication scheme for future digital wireless communications systems. Such wide-band systems usually operate in a frequency-selective fading channel that introduces intersymbol interference and thus potential performance degradation. Previously proposed subspace-based blind channel identification algorithms, which provide estimates of channel parameters for effective equalization, suffer from high numerical complexity for systems with large spreading gains. In this paper, it is shown that, through the use of matched filter outputs, reduction in numerical complexity can be obtained. The complexity reduction is considerable when the channel length is small and the system is moderately loaded. The results show that the new algorithm suffers a slight performance loss. Although the employed matched filter outputs do not form a set of sufficient statistics for the unknown channels, the difference between the matched filter outputs and the sufficient statistics becomes negligible for large observation lengths and the asymptotic normalized Fisher information does not change. Performance is evaluated through simulations, the derivation of a tight approximation of the mean-squared channel estimation error, and through comparisons to the Cramer-Rao bound for the estimation error variance. It is shown that the approximation of the mean-squared error can be obtained in terms of the correlation of the spreading codes and the channels. This representation of the error supplies a tool for investigating the relationship between performance and spreading sequence correlations.

Multichannel MLSE equalizer with parametric FIR channel identification

We propose a parametric finite impulse response (FIR) channel identification algorithm, apply the algorithm to a multichannel maximum likelihood sequential estimation (MLSE) equalizer using multiple antennas, and investigate the improvement in the overall bit error rate (BER) performance. By exploring the structure of the specular multipath channels, we are able to reduce the number of channel parameters to provide a better channel estimate for the MLSE equalizer. The analytic BER lower bounds of the proposed algorithm as well as those of several other conventional MLSE algorithms in the specular multipath Rayleigh-fading channels are derived. In the derivation, we consider the channel mismatch caused by the additive Gaussian noise and the finite-length channel approximation error. A handy-to-use simplified BER lower bound is also derived. Simulation results that illustrate the BER performance of the proposed algorithm in the global system for mobile communications (GSM) system are presented and compared to the analytic lower bounds.

Direct blind equalizers of multiple FIR channels: a deterministic approach

Blind equalization of single-input multi-output channels has practical value for inverse problems encountered in communications, sonar, and seismic data processing. Relying on diversity (sufficient number of multiple outputs), we bypass the channel estimation step and derive direct blind FIR equalizers of co-prime FIR channels. There are no constraints on the inaccessible input, apart from a minimum persistence of excitation condition; the input can be deterministic or random with unknown color or distribution. At moderate SNR (>20 dB), the resulting algorithms remain operational even with very short data records (<100 samples), which makes them valuable for equalization of rapidly fading multipath channels. Complexity, persistence of excitation order, and mean-square error performance tradeoffs are delineated for equalizers of single-shift (semi-blind), pair, or, multiple shifts estimated separately or simultaneously. Optimum and suboptimum combinations of the equalizers' outputs are also studied. Simulations illustrate the proposed algorithms and compare them with dual deterministic channel identification algorithms.

A Novel Blind Channel Identification and Equalisation AlgorithmBased on Maximum Likelihood

A novel blind non-decision directed maximum likelihood algorithm for fractionally-spaced nonminimum phase FIR channel identification and equalisation is presented. The algorithm results from using the low signal to noise approximation to the average of the likelihood function with respect to the transmitted data sequence. The channel estimation equation is derived in a closed form. The resulting algorithm has two distinct advantages. The first is that the channel estimates are asymptotically consistent, and the second is that the algorithm is computationally efficient since it only requires the calculation of one eignevector. Simulation results are presented to show the performance of the proposed algorithm.

Data-recursive algorithms for blind channel identification in oversampled communication systems

Data-recursive algorithms are presented for performing blind channel identification in oversampled communication systems. Novel on-line solutions with complexities that are only linear in the oversampling rate are considered, and mean convergence conditions are provided. Numerical results are presented for a binary phase-shift keyed (BPSK) system.

Blind multipath channel identification for wideband communications systems based on cyclostationary statistics

AbstractIn this letter a blind channel identification algorithm for broadband signals is proposed. Although we take CDM signals into consideration for simplicity, the algorithm can be easily extended to any other wideband communication system. The algorithm is based on cyclostationary properties of the CDM signal. Moreover a brief discussion on the identifiability of a generic channel transfer function from the measured cyclic and conjugate cyclic spectra of the received signal is also presented.

Asymptotically optimal blind fractionally spaced channel estimation and performance analysis

When the received data are fractionally sampled, the magnitude and phase of most linear time-invariant FIR communications channels can be estimated from second-order output only statistics. We present a general cyclic correlation matching algorithm for known order FIR blind channel identification that has closed-form expressions for calculating the asymptotic variance of the channel estimates. We show that for a particular choice of weights, the weighted matching estimator yields (at least for large samples) the minimum variance channel estimator among all unbiased estimators based on second-order statistics. Furthermore, the matching approach, unlike existing methods, provides a useful estimate even when the channel is not uniquely identifiable from second-order statistics.

Parameter estimation and blind channel identification in impulsive signal environments

New methods for parameter estimation and blind channel identification in impulsive signal environments are presented, where the signals/noise are modeled as symmetric /spl alpha/-stable (S/spl alpha/S) processes. First, we present methods for estimating the parameters (characteristic exponent /spl alpha/ and dispersion /spl gamma/) of a S/spl alpha/S distribution from a time series. The fractional lower order moments, with both positive and negative orders, and their applications to signal processing are introduced. Then we present a new algorithm for blind channel identification using the output fractional lower order moments, and the /spl alpha/-Spectrum, a new spectral representation for impulsive signals, is introduced. From the /spl alpha/-Spectrum, we establish the blind identifiability conditions of any FIR channel (mixed-phase, unknown order) with i.i.d. S/spl alpha/S (/spl alpha/>1) input. As a byproduct, a simple algorithm for recovering the phase of any type of a signal from the magnitude of its z-transform is presented. The novelty of our paper is in parameter estimation and blind identification of the FIR channel based on fractional lower order moments of its output data. Monte Carlo simulations clearly demonstrate the performance of the new methods.

A least-squares approach to blind channel identification

Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented.

A new blind time-domain channel identification method based on cyclostationarity

A blind channel identification scheme based on oversampling the channel output and employing a (discrete) cyclic correlation function similar to Gardner (1991) is proposed. It is shown that the known results on blind identifiability follow directly. Further, this connection suggests a new and efficient time-domain algorithm for blind channel identification that is applied to some representative examples. >

Performance analysis of channel identification algorithm with SVD prefiltering

A quantitative statistical analysis of the mean-squared error (MSE) performance of the RRLS algorithm that is summarised in Reference 1 of the Letter, for improving conventional least-square estimates of the truncated pulse response of a communication channel, is presented. Experimental results confirm the validity of the approximations that are used in the MSE computations.