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Keywords
(18)
Channel Estimation
Complexity Theory
Covariance Matrix Estimator
Factor Graph
Finite Impulse Response
Frequency Domain
Frequency Domain Analysis
Frequency Selective Channel
Least Square
Low Complexity
Message Passing
Recursive Estimation
Signal Processing
State Space
System Identification
Turbo Equalization
Fast Fourier Transform
Time Varying
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A Frequency Domain StateSpace Approach to LS Estimation and Its Application in Turbo Equalization
A Frequency Domain StateSpace Approach to LS Estimation and Its Application in Turbo Equalization,10.1109/TSP.2011.2135857,IEEE Transactions on Signa
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A Frequency Domain StateSpace Approach to LS Estimation and Its Application in Turbo Equalization
(
Citations: 1
)
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Qinghua Guo
,
Defeng Huang
A
frequency domain
statespace approach to the least squares (LS) estimation for
finite impulse response
(FIR)
system identification
is proposed by exploiting the Toeplitz structure of the data matrix, leading to two lowcomplexity FFT (fast Fourier transform)based unbiased recursive estimators. The proposed re cursive estimators are applied to the smoothing estimation of time varyingfrequencyselectivechannelsinturboequalizationsystems, and
channel estimation
approaches with complexity per symbol are developed, where is the number of channel taps. IndexTerms—Channelestimation,factorgraphs,Gaussianmes sagepassing,least squares,statespace approach, timevaryingfre quency selective channels, turbo equalization.
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 59, no. 7, pp. 32883300, 2011
DOI:
10.1109/TSP.2011.2135857
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Citation Context
(1)
...1) By carrying out channel estimation and detection jointly, the proposed lowcomplexity EM based approach can significantly outperform alternative approaches (with similar complexity) where channel estimation and detection are implemented separately, e.g., the approaches in [17]–[
19
] (in which the uncertainty due to the estimate of the symbols is accommodated into the additive noise for channel estimation, and vice versa for symbol ...
...The length of the frequency domain channel response in model (6) is , but the actual degree of freedom of the channel is (since the length of is ). This means that there are some constraints among the entries of , which should be properly exploited [
19
]...
...As in [
19
], to reduce the complexity, the following approximation is adopted in the forward covariance matrix updating for the multiplication node...
...In the approaches [17]–[
19
] where the channel estimation and detection are implemented separately, the complexity of detection is and that of channel estimation is (in [17] and [18]) or (in [19])...
...In the approaches [17]–[19] where the channel estimation and detection are implemented separately, the complexity of detection is and that of channel estimation is (in [17] and [18]) or (in [
19
])...
...The complexity of the proposed approach depends on ,b ut can be a small number, e.g., in the simulations shown in Section IV, the maximum of is only 5. The proposed approach can achieve significant performance improvement by using the EM iteration compared with the approaches in [17]–[
19
], which will be demonstrated in Section IV...
...As in [9] and [17]–[
19
], with the approximation that the channel is static within a short time duration, we partition the length signal...
...The MSIE performance and the BER performance of the proposed approach, the approach in [17], the approach in [18] (with approximate rule 1), and the approach in [
19
] (with CE2) are shown in Fig. 3. It can...
Qinghua Guo
,
et al.
EMBased Joint Channel Estimation and Detection for Frequency Selectiv...
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Regularized fast recursive least squares algorithms for adaptive filtering
(
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Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 39, no. 4, pp. 860871, 1991
Fast calculation of gain matrices for recursive estimation schemes
(
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LENNART LJUNG
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Journal:
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Citations
(1)
EMBased Joint Channel Estimation and Detection for Frequency Selective Channels Using Gaussian Message Passing
Qinghua Guo
,
Defeng Huang
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 59, no. 8, pp. 40304035, 2011