Blind identification schemes for multi-channel DS/SS systems (original) (raw)
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A subspace based blind channel identiÿcation algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made in this algorithm of either the statistics of the input sequence or even of the fact that the symbols are from a ÿnite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. Using this algorithm as a base and using the extra information which becomes available when the transmitted symbols are from a known ÿnite set, the EC-LS-Subspace algorithm is derived. The EC-LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal directly. In the noiseless case, if some conditions are met, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using an observation interval of just (J +2)T , if the length of the impulse response of a channel is JT; T being the symbol interval. In the noisy case, simulations have shown that the channel can be identiÿed accurately by using a very small observation interval (comparable to (J + 2)T).
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A deterministic maximum likelihood (DML) approach is presented for the blind channel estimation problem. It is first proposed in a block version, which consists of iterating two steps, each one solving a least-squares problem either in the channel or in the symbols. In the noiseless case and under certain conditions, this algorithm gives the exact channel and the exact symbol vector with a finite number of samples. It is shown that even if the DML method has a single global minimum, the proposed iterative procedure can converge to spurious local minima. This problem can be detected (under some channel diversity conditions) by using a numerical test that is proposed in the paper. Based on these considerations, we extend the maximum likelihood block algorithm (MLBA) to recursive implementations [maximum likelihood recursive algorithm (MLRA)]. The MLRA is able to track variations of the system by the introduction of an exponential forgetting factor in the DML criterion. The link between the adaptive algorithm and a soft decision feedback equalizer (SDFE) is emphasized. Low-complexity versions of the recursive and adaptive algorithm are presented.
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This paper considers the problem of recovering an unknown signal transmitted over an unknown (but stationary) multipath channel, and received by a narrowband array with unknown calibration. Unlike recently proposed multichannel blind equalization techniques, the methods described herein employ a model based on physical channel parameters rather than unstructured multiple output FIR lters. The algorithms exploit the structure of the signal and noise subspaces of the array output data when transformed to the frequency domain. Two approaches are presented. The rst is an ESPRIT-like solution that provides a closed-form, but suboptimal, blind signal estimate. The second is based on maximum likelihood and, though requiring a search, is easily initialized with the ESPRIT solution. A mathematical development of the two algorithms is given, and their advantages and disadvantages relative to other currently available techniques are discussed.
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This correspondence develops a new blind multichannel equalization (BME) scheme and proposes an appropriate cost function by the exploitation of some shifting properties of the autocorrelation matrices of the source. In addition, an efficient programming is considered based on a generalized rayleigh quotient formulation using a conjugate gradient algorithm.
IEEE Transactions on Circuits and Systems I: Regular Papers, 2013
Blind matched filter receiver is advantageous over the state-of-the-art blind schemes due the simplicity in its implementation. To estimate the multipath communication channels, it uses neither any matrix decomposition methods nor statistics of the received data higher than the second order ones. On the other hand, the realization of the conventional blind matched filter receiver requires the noise variance to be estimated and the equalizer parameters to be calculated in state-space with relatively costly matrix operations. In this paper, a novel architecture is proposed to simplify a potential hardware implementation of the blind matched filter receiver. Our novel approach transforms the blind matched filter receiver into an all-adaptive format which replaces all the matrix operations. Furthermore, the novel design does not need for any extra step to estimate the noise variance. In this paper we also report on a comparative channel equalization and channel identification scenario, looking into the performances of the conventional and our novel all-adaptive blind matched filter receiver through simulations.
9Th European Signal Processing Conference, 1998
The least-squares and the subspace methods are two well-known approaches for blind channel identification/ equalization. When the order of the channel is known, the algorithms are able to identify the channel, under the so-called length and zero conditions. Furthermore, in the noiseless case, the channel can be perfectly equalized. Less is known about the performance of these algorithms in the practically inevitable cases in which the channel possesses long tails of "small" impulse response terms. We study the performance of the m m mth-order least-squares and subspace methods using a perturbation analysis approach. We partition the true impulse response into the m m mth-order significant part and the tails. We show that the m m mth-order least-squares or subspace methods estimate an impulse response that is "close" to the m m mth-order significant part. The closeness depends on the diversity of the m m mth-order significant part and the size of the tails. Furthermore, we show that if we try to model not only the "large" terms but also some "small" ones, then the quality of our estimate may degrade dramatically; thus, we should avoid modeling "small" terms. Finally, we present simulations using measured microwave radio channels, highlighting potential advantages and shortcomings of the least-squares and subspace methods.
A blind multichannel identification algorithm robust to order overestimation
IEEE Transactions on Signal Processing, 2002
Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations.
IEEE Transactions on Signal Processing, 1999
The least-squares and the subspace methods are two well-known approaches for blind channel identification/ equalization. When the order of the channel is known, the algorithms are able to identify the channel, under the so-called length and zero conditions. Furthermore, in the noiseless case, the channel can be perfectly equalized. Less is known about the performance of these algorithms in the practically inevitable cases in which the channel possesses long tails of "small" impulse response terms. We study the performance of the m m mth-order least-squares and subspace methods using a perturbation analysis approach. We partition the true impulse response into the m m mth-order significant part and the tails. We show that the m m mth-order least-squares or subspace methods estimate an impulse response that is "close" to the m m mth-order significant part. The closeness depends on the diversity of the m m mth-order significant part and the size of the tails. Furthermore, we show that if we try to model not only the "large" terms but also some "small" ones, then the quality of our estimate may degrade dramatically; thus, we should avoid modeling "small" terms. Finally, we present simulations using measured microwave radio channels, highlighting potential advantages and shortcomings of the least-squares and subspace methods.