Blind and semi-blind maximum likelihood methods for FIR multichannel identification

Ayadi, Jaouhar; Carvalho, Elisabeth de; Slock, Dirk

doi:10.1109/icassp.1998.679541

Cited by 29 publications

(20 citation statements)

References 9 publications

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“…However, we found that this iterative heuristic does not work well. Instead, it is possible to use the gradient based approach, of [3]. We find that it reaches the optimum in very few steps.…”

Section: Introductionmentioning

confidence: 97%

High-Resolution Impulse Radio Ultra Wideband Ranging

Zhan

Ayadi

Farserotu

et al. 2007

2007 IEEE International Conference on Ultra-Wideband

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-We propose a high resolution ranging algorithm for unsynchronized impulse radio Ultra-wideband (UWB) communication systems in gaussian noise. We pose the ranging problem as a Maximum Likelihood (ML) estimation problem for the channel delays and attenuations and phase offset at receiver. Then we translate the obtained delay estimates into an estimate of the distance. The ML problem is very non linear and hard to solve. Some previous works focused on finding alternative estimation procedures, for example by denoising. In contrast, we tackle the ML estimation problem directly. First, we use the same transformation as the first step of Iterative Quadratic Maximum Likelihood (IQML) and transform the ML problem into another optimization problem that gets rid of the amplitude coefficients. Second, we solve the remaining optimization problem with a gradient descent approach ("pseudo-quadratic maximum likelihood"). We show that our algorithm performs significantly better than previously published heuristics. We tested the approach on a real non-line of sight system and obtained good accuracy.Index Terms -maximum likelihood estimator, pseudoquadratic maximum likelihood, impulse radio ultra-wideband

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Section: Introductionmentioning

confidence: 97%

High-Resolution Impulse Radio Ultra Wideband Ranging

Zhan

Ayadi

Farserotu

et al. 2007

2007 IEEE International Conference on Ultra-Wideband

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“…Depending on the hypothesis upon which the expression of the likelihood function is built, one can distinguish between three families of ML methods: deterministic ML, in which the data symbols are considered as deterministic disturbances, Gaussian ML, in which the data symbols are assumed to be Gaussian distributed, and stochastic ML, where the true distribution of the data symbols is exploited. Some deterministic ML methods are presented, for instance, in [10]. In [11], a theoretical comparison of the Cramer-Rao bounds (CRBs) indicates that Gaussian ML methods outperform deterministic ML methods.…”

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confidence: 99%

Gaussian maximum-likelihood channel estimation with short training sequences

Rousseaux

Leus

Stoica

et al. 2005

IEEE Trans. Wireless Commun.

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Abstract-In this paper, we address the problem of identifying convolutive channels using a Gaussian maximum-likelihood (ML) approach when short training sequences (possibly shorter than the channel impulse-response length) are periodically inserted in the transmitted signal. We consider the case where the channel is quasi-static (i.e., the sampling period is several orders of magnitude smaller than the coherence time of the channel). Several training sequences can thus be used in order to produce the channel estimate. The proposed method can be classified as semiblind and exploits all channel-output samples containing contributions from the training sequences (including those containing contributions from the unknown surrounding data symbols). Experimental results show that the proposed method closely approaches the Cramer-Rao bound and outperforms existing trainingbased methods (which solely exploit the channel-output samples containing contributions from the training sequences only). Existing semiblind ML methods are tested as well and appear to be outperformed by the proposed method in the considered context. A major advantage of the proposed approach is its computational complexity, which is significantly lower than that of existing semiblind methods.Index Terms-Block transmission, maximum-likelihood (ML) estimation, stationary multipath channel, training sequence.

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“…However, for synchronized code division multipple access (CDMA) communications, the computational complexity of this algorithm grows exponentially with the number of users and the size of the channel (in symbol duration) and is quite difficult (except when the number of users or channel length is very limited). Another kind of approach, based on deterministic or Gaussian ML methods, has been proposed by Slock et al [3], [6]. These methods lead to the minimization of a composite criterion defined as the sum of the classical training-based least-squares criterion and of the cost function associated with the blind deterministic or Gaussian ML method.…”

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confidence: 99%

A semi-blind channel estimation technique based on second-order blind method for CDMA systems

Lasaulce¹,

Loubaton²,

Moulines

2003

IEEE Trans. Signal Process.

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Abstract-This paper aims at studying a semi-blind channel estimation scheme based on the subspace method or a carefully weighted linear prediction approach. The corresponding (composite) semi-blind cost functions result from a linear combination of the training-based cost function and a blind cost function. For each blind method, we show how to calculate the asymptotic estimation error. Therefore, by minimizing this error, we can properly tune the -dimensional regularizing vector introduced in the composite semi-blind criterion (for active users in the uplink). The asymptotic estimation error minimization is a -variable minimization problem, which is a complex issue with which to deal. We explicitly show under what conditions this problem boils down to single-variable minimization problems. Our discussion is not limited to theoretical analyses. Simulation results performed in a realistic context [universal mobile telecommunication system-time division duplex (UMTS-TDD) mode] are provided. In particular, we conclude about the potential of the proposed approach in real communication systems.

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Blind and semi-blind maximum likelihood methods for FIR multichannel identification

Cited by 29 publications

References 9 publications

High-Resolution Impulse Radio Ultra Wideband Ranging

High-Resolution Impulse Radio Ultra Wideband Ranging

Gaussian maximum-likelihood channel estimation with short training sequences

A semi-blind channel estimation technique based on second-order blind method for CDMA systems

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