Structured channel estimation based decision feedback equalizers for sparse multipath channels with applications to digital TV receivers

Özen, Serdar; Hillery, W.J.; Zoltowski, M.D.; Nereyanuru, S.M.; Fimoff, M.

doi:10.1109/acssc.2002.1197243

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…Sparsity is a characteristic which can be found in both natural and man-made systems [27], [28]. Some of its realworld applications are data compression, acoustic echo cancellation, digital TV transmission, multipath channels and underwater acoustic communications [29], [30], [31], [32], [33]. The design of such algorithms has become a challenging and attractive research agenda recently, where it is shown that through considering the sparsity of a system, the estimation performance can be further improved.…”

Section: A Related Workmentioning

confidence: 99%

A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks

Pouradabi

Rastegarnia

Zandi

et al. 2021

Circuits Syst Signal Process

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This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalised subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variation, namely, diffusion proportionate normalised subband adaptive filter (DPNSAF), diffusion µ-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms.

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Section: A Related Workmentioning

confidence: 99%

A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks

Pouradabi

Rastegarnia

Zandi

et al. 2021

Circuits Syst Signal Process

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“…For instance the 8-VSB digital TV system has 728 training symbols, whereas the delay spreads of the terrestrial channels have been observed to be at least 400-500 symbols long. 5,6 The a-BLUE algorithm can be used as an initializer to the BLUE iterations, 7 or as a stand-alone alternative 8 approach that produces results of nearly the same quality as the results produced by the BLUE algorithm while at the same time requiring much less computational complexity (i.e., requiring about the same number of multiplications necessary to implement ordinary least squares) and having storage requirements similar to that of ordinary least squares.…”

Section: Introductionmentioning

confidence: 98%

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

Özen

Pladdy²,

Nerayanuru³

et al. 2004

SPIE Proceedings

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We provide an iterative and a non-iterative channel impulse response (CIR) estimation algorithm for communication receivers with multiple-antenna. Our algorithm is best suited for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols, and the receivers for this particular system are expected work in a severe frequency selective multipath environment with long delay spreads relative to the length of the training sequence. The iterative procedure calculates the (semi-blind) Best Linear Unbiased Estimate (BLUE) of the CIR. The non-iterative version is an approximation to the BLUE CIR estimate, denoted by a-BLUE, achieving almost similar performance, with much lower complexity. Indeed we show that, with reasonable assumptions, a-BLUE channel estimate can be obtained by using a stored copy of a pre-computed matrix in the receiver which enables the use of the initial CIR estimate by the subsequent equalizer tap weight calculator. Simulation results are provided to demonstrate the performance of the novel algorithms for 8-VSB ATSC Digital TV system. We also provide a simulation study of the robustness of the a-BLUE algorithm to timing and carrier phase offsets.

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“…In particular we show that a-BLUE initial channel estimation algorithm significantly outperforms the standard least squares and correlation based initial channel estimation algorithms achieving the same computational complexity. This feature makes the a-BLUE algorithm an attractive choice for receivers employing channel estimate based (indirect) equalizers [5], or for receivers with direct adaptive equalizers where a quick and reliable channel information is needed for equalizer tap weight initialization.…”

Section: Discussionmentioning

confidence: 99%

“…The novel algorithm presented in the sequel is targeted for the systems that are desired to work with channels having long delay spreads L d ; in particular we consider the case where (NT + 1)/2 < L d < NT , where NT is the duration of the available training sequence. For instance the 8-VSB digital TV system has 728 training symbols, whereas the delay spreads of the terrestrial channels have been observed to be at least 400-500 symbols long [4], [5]. The a-BLUE algorithm can be used as an initializer to the BLUE iterations [7], or as a stand-alone alternative [6] approach that produces results of nearly the same quality as the results produced by the BLUE algorithm while at the same time requiring much less computational complexity (i.e., requiring about the same number of multiplications necessary to implement ordinary least squares) and having storage requirements similar to that of ordinary least squares.…”

Section: Introductionmentioning

confidence: 99%

Approximate Best Linear Unbiased Channel Estimation for Frequency Selective Channels with Long Delay Spreads: Robustness to Timing and Carrier Offsets

Özen

Nerayanuru

Pladdy

et al.

2005 IEEE 61st Vehicular Technology Conference

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Abstract-We provide an iterative and a non-iterative channel impulse response (CIR) estimation algorithm for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. The iterative procedure calculates the (semi-blind) Best Linear Unbiased Estimate (BLUE) of the CIR. The non-iterative version is an approximation to the BLUE CIR estimate, denoted by a-BLUE, achieving almost similar performance, with much lower complexity. Indeed we show that, with reasonable assumptions, a-BLUE channel estimate can be obtained by using a stored copy of a pre-computed matrix in the receiver which enables the use of the initial CIR estimate by the subsequent equalizer tap weight calculator. Simulation results are provided to demonstrate the performance of the novel algorithms for 8-VSB ATSC Digital TV system. We also provide a simulation study of the robustness of the a-BLUE algorithm to timing and carrier phase offsets. KEY WORDS: channel estimation, least squares, best linear unbiased estimator, signal processing for communications applications, carrier and timing offsets

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Structured channel estimation based decision feedback equalizers for sparse multipath channels with applications to digital TV receivers

Cited by 12 publications

References 2 publications

A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks

A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

Approximate Best Linear Unbiased Channel Estimation for Frequency Selective Channels with Long Delay Spreads: Robustness to Timing and Carrier Offsets

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