A second-order blind equalization method robust to ill-conditioned SIMO FIR channels

Xiang, Yong; Yang, Liu; Peng, Dezhong; Xie, Shengli

doi:10.1016/j.dsp.2014.05.015

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…The seminal contributions have been presented in [1,2]. Since then, numerous solutions or variants have been proposed, depending on the considered available information nature ( [3][4][5][6][7][8][9]), as well as the considered design framework ( [10][11][12][13]). The available information consists in second-order and high-order statistics, while the design is carried out in a time domain or a frequency domain.…”

Section: Considered Equalisation Problemmentioning

confidence: 99%

Blind equalisation in the presence of bounded noise

Moussa

Pouliquen

Frikel

et al. 2018

IET signal process.

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This study addresses the blind equalisation problem in the presence of bounded noise using an optimal bounding ellipsoid algorithm. This provides an adequate blind equalisation algorithm with an accurate parameter estimation. A fundamental analysis of the involved equaliser is performed to emphasise its underlying properties. This fundamental result is corroborated by promising simulation results. where n h , h i , {e k }, and {s k } are, respectively, the order, the complex channel tap weight, the unknown noise, and the input symbols. The channel can be a single-input multi-output channel with p ≥ 1 outputs. This means that x k ∈ ℂ p × 1 , e k ∈ ℂ p × 1 , and h i ∈ ℂ p × 1. The following assumptions complete the description of the problem: A.1 The input symbols {s k } are independent, identically distributed. They are drawn from a known constellation C [quadrature amplitude modulation (QAM), phase-shift keying (PSK), or amplitude-shift keying (ASK)]. A.2 The noise sequence is bounded, i.e. |e k (j) | ≤ δ e (j) for all j ∈ {1, …, p}, where e k (j) is the jth component of e k and |. | is the complex modulus. A.3 The sequence of variables {s k } and {e k (j) } are independent for all j ∈ {1, …, p}.

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Section: Considered Equalisation Problemmentioning

confidence: 99%

Blind equalisation in the presence of bounded noise

Moussa

Pouliquen

Frikel

et al. 2018

IET signal process.

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show abstract

“…In digital communication systems, digital signal crosstalk the generated between codes in the process of transmission, because the channel factors of multipath propagation and fading [1][2]. Therefore, People using the channel equalization algorithm to eliminate crosstalk between codes.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Blind Equalization Algorithm Based on Feed Forward Neural Network

Zhao¹

2015

Proceedings of the First International Conference on Information Science and Electronic Technology

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Existing neural network algorithms have the problems of slow convergence and low accuracy. In response to this phenomenon, this paper presents a neural network blind equalization algorithm based on feed-forward neural network. And we proposed feed-forward neural network blind equalization algorithm by research of traditional neural network blind equalization algorithm. And it is using a feed-forward neural network of the hidden layer to approximate the objective function. At last, we by combining the cost functions of feedforward network to correct the acquired information. Experimental results show that the experimental results basically consistent with the expected results. By comparison with other algorithms, this algorithm has better convergence and accuracy.

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“…Channel identification has always been an important part of digital communication systems, however a several results have been done in the literature for identification of Finite Impulse Response (FIR) which they employ different techniques [Safi et al, 2011], [Nabiha and Hassani, 2009]. Some classes of algorithms for channel estimation are based on iterative strategy and some of them are focused on the structural methods of the received observation [Darsena et al, 2004], [Yong et al, 2014], [Peng et al, 2009].…”

Section: Introductionmentioning

confidence: 99%

Channel Identification and Equalization based on Kernel Methods for Downlink Multicarrier-CDMA Systems

Bouikhalene

Boutalline

Safi

2015

Journal of Electronic Commerce in Organizations

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In this paper the authors are focused on channel identification and equalization for Multi-Carrier Code Division Multiple Access (MC-CDMA) system. For this, they identify the impulse response of two practical selective frequency fading channels called Broadband Radio Access Network (BRAN A and BRAN B) normalized by the European Telecommunications Standards Institute (ETSI). To identify the channel parameters, they have the positive definite kernels to build on algorithm. The simulations show that the presented method confirms the good performance for different SNR values. In part of equalization, the authors use the Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) equalizers.

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A second-order blind equalization method robust to ill-conditioned SIMO FIR channels

Cited by 11 publications

References 33 publications

Blind equalisation in the presence of bounded noise

Blind equalisation in the presence of bounded noise

Neural Network Blind Equalization Algorithm Based on Feed Forward Neural Network

Channel Identification and Equalization based on Kernel Methods for Downlink Multicarrier-CDMA Systems

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