Blind channel estimation exploiting transmission filter knowledge

Ayadi, Jaouhar; Slock, Dirk

doi:10.1016/s0165-1684(00)00069-4

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Recently, Ayadi and Stock [2] discussed the multipath identification problem and suggested an LP-based approach as one choice. In this paper, we theoretically analyze the LP-based multipath channel identification approach.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multipath Channel Identification Algorithm Using Mulichannel Linear Prediction

Wang

Chow

2004

Circuits, Systems, and Signal Processing

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Blind channel identification exploits the measurable channel output signal and some a priori knowledge of the statistics of the channel input signal. However, in many scenarios, more side information is available. In digital communication systems, the pulseshaping filter in the transmitter and the antialiasing filter in the receiver are often known to the receiver. Exploitation of this prior knowledge can simplify the channel identification problem. In this paper, we provide a thorough theoretical analysis on the linear prediction (LP)-based multipath identification approach. The analysis shows that the order overdetermination greatly deteriorates the performance of the LP multipath identification approach. This article, then, proposes an effective approach to detect the order of the multipath channel and alleviate the performance degradation attributed to the channel overmodeling. The obtained results demonstrate that the proposed approach offers substantial performance gain.

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Section: Introductionmentioning

confidence: 99%

An Improved Multipath Channel Identification Algorithm Using Mulichannel Linear Prediction

Wang

Chow

2004

Circuits, Systems, and Signal Processing

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“…Recently, Ayadi et al [12] discussed the multipath identification problem and suggested an LP-based approach as one choice. In this brief, we examine a linear-prediction-based multipath channel identification approach.…”

Section: Introductionmentioning

confidence: 99%

“…LetProof: We first prove z z z = [ h h h T 0 0 0 T ] is a solution to(12). Expanding the multiplication of S S S by […”

mentioning

confidence: 99%

Linear prediction based multipath channel identification algorithm

Chow

Wang

2003

IEEE Trans. Circuits Syst. I

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769were introduced to ensure the ZVS operation [7], [8]. However, there are still ranges of load impedance with the non-ZVS operation. This prediction circuit may be used as a part of the protection circuit, which stops the non-ZVS operation and destruction of the switching device.Abstract-In digital communication systems, the pulse-shaping filter in the transmitter and the anti-aliasing filter in the receiver are often known. In this brief, this a priori knowledge is exploited to simplify the channel identification problem. The multipath identification problem is formulated as a homogeneous linear equation, in which a matrix is formed from the optimal linear predictor. Instead of solving directly the underlying linear equation, the estimate is obtained by finding the minor eigenvector of a matrix. The presented analysis shows that the performance of the proposed approach degrades significantly when the order is over estimated. An effective approach is then proposed to remove the redundant components in the estimated multipath channel vector. The obtained results show that the proposed approach is able to enhance the performance of the linear programming approach significantly when the order is over estimated.

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On linear channel-based noise subspace parameterizations for blind multichannel identification

Ayadi

Slock²

2001 IEEE Third Workshop on Signal Processing Advances in Wireless Communications (SPAWC'01). Workshop Proceedings (Cat. No.01E

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Blind channel estimation exploiting transmission filter knowledge

Cited by 3 publications

References 26 publications

An Improved Multipath Channel Identification Algorithm Using Mulichannel Linear Prediction

An Improved Multipath Channel Identification Algorithm Using Mulichannel Linear Prediction

Linear prediction based multipath channel identification algorithm

On linear channel-based noise subspace parameterizations for blind multichannel identification

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