Approximate best linear unbiased channel estimation for frequency selective multipath channels with long delay spreads

Özen, Serdar; Pladdy, C.; Fimoff, M.; Nerayanuru, S.M.; Zoltowski, M.D.

doi:10.1109/acssc.2003.1292099

Cited by 10 publications

(11 citation statements)

References 7 publications

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“…We present simulation examples that demonstrate the superior performance of an adaptive DFE for a Doppler channel after initialization using the iterative algorithm. In each case, for the simulations, we make a comparison between the correlation method of channel estimation and the approximate method given in [27].…”

Section: Discussionmentioning

confidence: 99%

“…Finally, we compute h (k+1) using (15 Compute channel estimate h (k+1) using (15) end for Practical low-complexity methods for solving (15) in the for loop must be determined. We are presently working on such low-complexity approximations to this part of the algorithm [27]- [30]. We show the simulation results for channels used in the high-definition TV (HDTV) literature, which are known as the Brazilian channels.…”

Section: Iterative Algorithm For Finding the Blue Channel Estimatementioning

confidence: 99%

“…We do not give the specific architecture used here. The plots are given for the three separate cases, namely 1) initializing the DFE with the iterative BLUE estimate after one iteration of our algorithm (denoted as BLUE in the figure), 2) initializing the DFE with the approximate BLUE estimate given in [27], (denoted as aBLUE in the figure), and 3) initializing the DFE with the correlation estimate of the CIR (denoted as XCORR in Fig. 2).…”

Section: B Symbol Error Rate For Brazil-dmentioning

confidence: 99%

See 2 more Smart Citations

Semiblind BLUE Channel Estimation With Applications to Digital Television

Pladdy¹,

OzenOzen²,

Nerayanuru

et al. 2006

IEEE Trans. Veh. Technol.

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Abstract-A semiblind iterative algorithm to construct the best linear unbiased estimate (BLUE) of the channel impulse response (CIR) vector h for communication systems that utilize a periodically transmitted training sequence within a continuous stream of information symbols is devised. The BLUE CIR estimate for the general linear model y = Ah + w, where w is the correlated noise, is given by the Gauss-Markoff theorem. The covariance matrix of the correlated noise, which is denoted by C(h), is a function of the channel that is to be identified. Consequently, an iteration is used to give successive approximations h (k) , k = 0, 1, 2, . . . to h BLUE , where h (0) is an initial approximation given by the correlation processing, which exists at the receiver for the purpose of frame synchronization. A function F (h) for which h BLUE is a fixed point is defined. Conditions under which h BLUE is the unique fixed point and for which the iteration proposed in the algorithm converges to the unique fixed point h BLUE are given. The proofs of these results follow broadly along the lines of Banach fixed-point theorems.

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Section: Discussionmentioning

confidence: 99%

Section: Iterative Algorithm For Finding the Blue Channel Estimatementioning

confidence: 99%

Section: B Symbol Error Rate For Brazil-dmentioning

confidence: 99%

See 1 more Smart Citation

Semiblind BLUE Channel Estimation With Applications to Digital Television

Pladdy¹,

OzenOzen²,

Nerayanuru

et al. 2006

IEEE Trans. Veh. Technol.

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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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“…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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Approximate best linear unbiased channel estimation for frequency selective multipath channels with long delay spreads

Cited by 10 publications

References 7 publications

Semiblind BLUE Channel Estimation With Applications to Digital Television

Semiblind BLUE Channel Estimation With Applications to Digital Television

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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