Improving the Stability of the DFT Error Recovery Codes by Using the Vandermonde Fast Decoding Algorithm

Momenai, A.; Talebi, Siamak

doi:10.1109/icassp.2006.1660789

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…However, if the erasures are bursty, the above algorithm may become unstable. To combat bursty erasures, we can use the SDFT [2], [53], [109], [110], [107] instead of DFT. The simulation results for block codes with erasure and impulsive noise channels are given in the following two subsections.…”

Section: A Decoding Of Block Codes-elp Methodsmentioning

confidence: 99%

A Unified Approach to Sparse Signal Processing

Marvasti,

Amini,

Haddadi

et al. 2009

Preprint

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A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed.The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding in finite and real Galois fields is then related to over-sampling with similar reconstruction algorithms. The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and Error Locator Polynomials in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method. Such narrowband spectral estimation is then related to multi-source location and direction of arrival estimation in array processing. The notions of sparse array beamforming and sparse sensor networks are also introduced. Sparsity in unobservable source signals is also shown to facilitate source separation in Sparse Component Analysis (SCA); the algorithms developed in this area are also widely used in compressed sensing. Finally, the nature of the multipath channel estimation problem is shown to have a sparse formulation; algorithms similar to sampling and coding are used to estimate typical multicarrier communication channels. I. INTRODUCTIONT HERE are many applications in signal processing and communication systems where the discrete signals are sparse in some domain such as time, frequency, or space i.e., most of the samples are zero, or alternatively their transform in another domain (normally called "frequency coefficients")

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Section: A Decoding Of Block Codes-elp Methodsmentioning

confidence: 99%

A Unified Approach to Sparse Signal Processing

Marvasti,

Amini,

Haddadi

et al. 2009

Preprint

View full text Add to dashboard Cite

show abstract

“…But their stability falls when the quantization is used or when there is a large number of consecutive errors. References [3]- [9] have studied this behavior and proposed some methods to avoid this situation. Some of them improved the decoding algorithms and some of them have suggested different methods of interleaving transmitted data to convert the consecutive errors into sparse errors and maintain the stability of the DFT codes.…”

Section: Introductionmentioning

confidence: 96%

“…Some of them improved the decoding algorithms and some of them have suggested different methods of interleaving transmitted data to convert the consecutive errors into sparse errors and maintain the stability of the DFT codes. But [3]- [9] have considered only one special and simple case of the data oversampling in the encoder. They have mostly concentrated on the decoder when the zeroes are inserted consecutively in the time or Fourier domain of the original signal.…”

Section: Introductionmentioning

confidence: 99%

Introducing stable oversampling patterns for DFT error recovery codes in bursty erasure channels

Momenai

Talebi

2007

2007 9th International Symposium on Signal Processing and Its Applications

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Discrete Fourier Transform (DFT) error recovery codes like Reed Solomon codes in the Finite Field are widely used in the communication systems. But the DFT codes in the real and complex fields of numbers are very unstable in the burst of error situations. This paper proposes to change the oversampling patterns in the encoder from the consecutive zero padding to sparse zero padding. It will also prove that the DFT codes based on this type of oversampling are decodable and stable in the case of burst of errors. The simulation results also show the robustness of these codes against quantization error and burst of errors.

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“…This overdetermined linear system can be formulated as square linear system A x = b, where: A = " W H W; (13) b = " W HWx ;…”

Section: Preliminariesmentioning

confidence: 99%

“…On the other side, the lost samples of an oversampled signal can be reconstructed by building some equations based on valid samples [13][14][15][16]. In these methods, if the out-of-band components of the signal are removed completely, the reconstruction procedure fails [15,16].…”

Section: Introductionmentioning

confidence: 99%

Amplitude reconstruction of clipped OFDM by using DFT-based least squares

Ahmadi

Talebi

2017

Scientia Iranica

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Abstract. OFDM is an e ective multicarrier transmission technique with one primary disadvantage; it su ers from high Peak-to-Average Power Ratio (PAPR). Although clipping and ltering is a simple and e ective method for PAPR reduction, it makes in-band and out-of-band noise, which degrades the bit error rate performance and spectral e ciency. Publications on this subject show that clipped samples could be reconstructed at the receiver by using oversampled signal and bandwidth expansion. By building on published literature, this paper aims to achieve a low-complexity method. The proposed method has complexity order of O(L 2 ) to solve linear system, where L indicates the number of clipped samples. Simulation results con rm that our proposed method leads to both a better biterror rate performance and a lower complexity than similar methods. These results also show that our method o ers adequate performance, especially at low clipping ratios.

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Improving the Stability of the DFT Error Recovery Codes by Using the Vandermonde Fast Decoding Algorithm

Cited by 4 publications

References 11 publications

A Unified Approach to Sparse Signal Processing

A Unified Approach to Sparse Signal Processing

Introducing stable oversampling patterns for DFT error recovery codes in bursty erasure channels

Amplitude reconstruction of clipped OFDM by using DFT-based least squares

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