Academic
Publications
Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems

Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems,10.1109/TCSI.2009.2023765,IEEE Transactions on Circuits and Sy

Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems   (Citations: 4)
BibTex | RIS | RefWorks Download
In this paper, a very efficient semi-blind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequency-division 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 in-depth study of the second-order statistics of the signal received through a noise-free 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 pilot-assisted LS estimation to detect the MSTs in a semi-blind fashion. It os also shown that the new MST detection algorithm can be extended for the estimation of multiple-input–multiple-output (MIMO)–OFDM channels. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semi-blind approach.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
    • ...Based on second-order 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 noise-free 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 training-based 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 Wanet al. Semiblind Sparse Channel Estimation for MIMO-OFDM Systems

    • ...It is proved that conventional channel estimation methods provide higher errors because they ignore the prior knowledge of the sparseness [3]...

    Eva Lagunaset al. Sparse channel estimation based on compressed sensing for ultra wideba...

    • ...In our previous work [9], based on an analysis of the second-order statistics of the received signal passing through a sparse channel, an efficient semi-blind 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 Wanet al. Applying Csiszár's I-divergence to blind sparse channel estimation

Sort by: