Compressive sensing based joint frequency offset and channel estimation for OFDM

Abstract

We consider joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) for orthogonal frequency division multiplexing (OFDM) with pilot symbols. A new method based on compressed sensing is proposed. It has been shown that the CIR can be represented as a 1-block sparse signal by using a dictionary constructed by concatenating subspaces of CFO values taken from a search space. Recovery of both CFO and CIR is accomplished by the block orthogonal matching pursuit algorithm. The proposed method uses only one OFDM training block and does not require any initialization. The performance of the proposed method is compared against the well-established pilot based estimators: Moose, Classen, the maximum likelihood estimator, and the p-algorithm. Numerical results show that the performance of the proposed method does not depend on the value of the CFO. We also give worst-case upper bounds for the mean squared error of the CIR estimate for a sparse multipath channel.

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems

“…From Equation (4), the corresponding pilot vectors of the m-th received OFDM symbol can be obtained as 20) is the ICI item. Substituting Equation (19) into Equation (20), and then performing simple algebraic manipulations, we can obtain…”

“…The second term on the right-hand side of Equation ( 20) is the ICI item. Substituting Equation (19) into Equation (20), and then performing simple algebraic manipulations, we can obtain…”

Joint carrier frequency offset and channel impulse response estimation for linear periodic channels

“…Sparsity pattern estimation is carried out using the version of the BPDN algorithm detailed in Section III-A with σ 2 = 1.05 · E w 2 and = 0.005. For the RCS-AML estimator (12), if |K|> N 0 , then K is truncated to contain only the indexes corresponding to the N 0 most dominant entries ofh. We consider two values of N obs corresponding to N p ∈ {4, 6}.…”

“…Indeed, in [4], it was shown that applying CS improves the performance of CIR estimation for wireless channels as well as the spectral efficiency. The works [11], [12] proposed a CS-based joint CIR and CFO estimator for slow-fading wireless LTI channels with AWGN. However, in [11] it was assumed that the number of non-zero CIR coefficients is a-priori known, and in [12] it was assumed that the number of observations is larger than the number of estimated parameters.…”

“…The works [11], [12] proposed a CS-based joint CIR and CFO estimator for slow-fading wireless LTI channels with AWGN. However, in [11] it was assumed that the number of non-zero CIR coefficients is a-priori known, and in [12] it was assumed that the number of observations is larger than the number of estimated parameters. Lastly, the work [13] designed an estimator for LPTV CIRs with temporally uncorrelated ACGN, assuming that the noise variance, the CFO, and the sparsity pattern of the CIR coefficients are all a-priori known.…”