This paper addresses the problem of sparse channel estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) systems. We propose to extend a recently proposed tap-tuned threshold-based Channel Impulse Response (CIR) structure detection scheme to the case of fast varying channels, through the use of a uniform pilot subcarriers placement. The chosen thresholds minimize the estimation Mean Squares Error (MSE) per tap. We also incorporate a local measure of the sparsity level which relies on the coarse Least Squares (LS) CIR estimate coefficients energies. The structured Channel Frequency Response (CFR) estimate is then exploited to demodulate the data subcarriers. Simulation results demonstrate the good denoising capacity of the proposed scheme.
International audienceIn this paper, we propose a new code-aided (CA) timing recovery algorithm for various linear constant modulus constellations based on the Maximum Likelihood (ML) estimator. The first contribution is the derivation of a soft estimator expression of the transmitted symbol instead of its true or hard estimated value which is fed into the timing error detector (TED) equation. The proposed expression includes the Log-Likelihood Ratios (LLRs) obtained from a turbo decoder. Our results show that the proposed CA approach achieves almost as good results as the data-aided (DA) approach over a large interval of SNR values while achieving a higher spectral efficiency. We also derive the corresponding CA Cramer Rao Bounds (CRB) for various modulation orders. Contrarily to former work, we develop here the CRB analytical expression for different M-PSK modulation orders and validate them through comparison to empirical CRB obtained by Monte Carlo iterations. The proposed CA estimator realizes an important gain over the non data-aided approach (NDA) and achieves a smaller gap when compared to its relative CA CRB, especially at moderate SNR values where modern systems are constrained to work
International audience— * In this paper, we propose a maximum likelihood based Code-Aided (CA) timing recovery algorithm for square-QAM modulated signals. We also theoretically derive the analytical expression of the CA Cramer-Rao Bound for time delay estimation. Our simulations show that the proposed CA approach realizes a performance equivalent to the Data-Aided (DA) approach over a large interval of signal to noise ratio (SNR) values
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