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Keywords
(8)
Channel Estimation
Computer Simulation
Correlation Function
Detection Algorithm
Least Square
Multiple Input Multiple Output
Orthogonal Frequency Division Multiplex
Second Order Statistics
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SemiBlind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems
SemiBlind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems,10.1109/TCSI.2009.2023765,IEEE Transactions on Circuits and Sy
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SemiBlind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems
(
Citations: 4
)
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Feng Wan
,
W.P. Zhu
,
M. N. S. Swamy
In this paper, a very efficient semiblind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequencydivision multiplexing (OFDM)
channel estimation
is developed. The
least square
(LS) estimation problem of sparse OFDM channels is first formulated, showing that the key to sparse
channel estimation
lies in the detection of the MSTs. An indepth study of the secondorder statistics of the signal received through a noisefree sparse OFDM channel reveals the sparsity and other properties of the correlation functions of the received signal. These properties lead to a direct relationship between the positions of the MSTs of the sparse channel and the most significant lags of the correlation functions, which is then used in conjunction with a pilotassisted LS estimation to detect the MSTs in a semiblind fashion. It os also shown that the new MST
detection algorithm
can be extended for the estimation of multipleinput–multipleoutput (MIMO)–OFDM channels. A number of computersimulationbased experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semiblind approach.
Journal:
IEEE Transactions on Circuits and Systems Iregular Papers  IEEE TRANS CIRCUIT SYSTI
, vol. 57, no. 3, pp. 703713, 2010
DOI:
10.1109/TCSI.2009.2023765
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Citation Context
(3)
...Based on secondorder statistics of the received signal, a very efficient semiblind MST detection algorithm that requires only a small number of OFDM symbols and pilot subcarriers has been developed in our previous work [
30
]...
...Obviously, (12) includes the autocorrelation matrix of y(n) as a special case when l =0 . It has been proved in [
30
] that, for the noisefree case, R(l) can be expressed in terms of the effective sparse channel matrix Z(d), d =0 , 1 ,...,D − 1. Using (2), (3), and (11) in (12), we obtain...
...with x(n) Δ x1(n) ,x 2(n) ,...,x NT (n)] T . By assuming a unit signal variance, i.e., σ 2 =1 , we can further prove [
30
]...
...MSLs, we have proposed in [
30
] a highly efficient semiblind algorithm for the first step of sparse channel estimation, i.e., the MST detection...
...However, for the second step, the estimation of the effective channel, e.g., the method in [
30
] and most of the existing sparse channel estimation methods, e.g., in [21]‐ [24], [26], [28], [36], and [37], relies on trainingbased estimation...
...R2,0 can be calculated based on the estimate of Z(d) using the sparse LS method in [
30
]...
...The parameter Ke in the MST detection algorithm in [
30
] is set to 0.8...
Feng Wan
,
et al.
Semiblind Sparse Channel Estimation for MIMOOFDM Systems
...It is proved that conventional channel estimation methods provide higher errors because they ignore the prior knowledge of the sparseness [
3
]...
Eva Lagunas
,
et al.
Sparse channel estimation based on compressed sensing for ultra wideba...
...In our previous work [
9
], based on an analysis of the secondorder statistics of the received signal passing through a sparse channel, an efficient semiblind nonzero tap detection algorithm was developed for OFDM channel estimation...
...In particular, we have shown [
9
] that ˆ (m) can be expressed in terms of the effective channel z (d) in the absence of noise as follows,...
Feng Wan
,
et al.
Applying Csiszár's Idivergence to blind sparse channel estimation
References
(25)
Design and Implementation of MIMOOFDM Baseband Processor for HighSpeed Wireless LANs
(
Citations: 21
)
Yunho Jung
,
Jiho Kim
,
SeongjooLee
,
Hongil Yoon
,
Jaeseok Kim
Journal:
IEEE Transactions on Circuits and Systems Iiexpress Briefs  IEEE TRANS CIRCUIT SYSTII
, vol. 54, no. 7, pp. 631635, 2007
Optimal training design for MIMO OFDM systems in mobile wireless channels
(
Citations: 305
)
Imad Barhumi
,
Geert Leus
,
Marc Moonen
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 51, no. 6, pp. 16151624, 2003
Blind Channel Estimation for MIMOOFDM Systems
(
Citations: 39
)
Changyong Shin
,
Robert W. Heath
,
Edward J. Powers
Journal:
IEEE Transactions on Vehicular Technology  IEEE TRANS VEH TECHNOL
, vol. 56, no. 2, pp. 670685, 2007
Blind Channel Estimation for MIMO OFDM Systems via Nonredundant Linear Precoding
(
Citations: 17
)
Feifei Gao
,
Arumugam Nallanathan
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 55, no. 2, pp. 784789, 2007
BlindChannel Identification for MIMO SingleCarrier ZeroPadding BlockTransmission Systems
(
Citations: 11
)
YiSheng Chen
,
ChingAn Lin
Journal:
IEEE Transactions on Circuits and Systems Iregular Papers  IEEE TRANS CIRCUIT SYSTI
, vol. 55, no. 6, pp. 15711579, 2008
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Citations
(4)
Semiblind Sparse Channel Estimation for MIMOOFDM Systems
Feng Wan
,
WeiPing Zhu
,
M. N. S. Swamy
Journal:
IEEE Transactions on Vehicular Technology  IEEE TRANS VEH TECHNOL
, vol. 60, no. 6, pp. 25692582, 2011
Joint blind channel estimation for MIMO OFDM systems via nonredundant linear precoding
S. Ghadrdan
,
S. Salari
,
M. Ahmadian
Conference:
International Conference on Communications and Information Technology  ICCIT
, 2011
Sparse channel estimation based on compressed sensing for ultra wideband systems
Eva Lagunas
,
Montse Najar
Conference:
IEEE International Conference on UltraWideband  ICU
, pp. 365369, 2011
Applying Csiszár's Idivergence to blind sparse channel estimation
Feng Wan
,
Urbashi Mitra
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 29242927, 2011