In this paper, we address the problem of OFDM channel estimation in the presence of phase noise (PHN) and carrier frequency offset (CFO). In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem.Nevertheless, in all these existing schemes, both the PHN and the Additive White Gaussian Noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either off-line or on-line estimators. In the on-line case, we propose Sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions and also noise power estimation. Index TermsOrthogonal frequency-division multiplexing (OFDM), channel estimation, phase noise, carrier frequency offset, sequential Monte-Carlo methods, Rao-blackwellization, optimal importance function, on-F. Septier and A. Menhaj-Rivencq are with the IEMN-DOAE UMR 8520, university of Valenciennes

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We address the problem of phase noise (PHN) and carrier frequency offset (CFO) mitigation in multicarrier receivers. In multicarrier systems, phase distortions cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the symbol detection stage. Here, we propose a non-pilot-aided scheme to jointly estimate PHN, CFO, and multicarrier signal in time domain. Unlike existing methods, non-pilot-based estimation is performed without any decision-directed scheme. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and an approximate optimal importance function for phase distortion sampling. Moreover, in order to fully benefit from time-domain processing, we propose a multicarrier signal model which includes the redundancy information induced by the cyclic prefix, thus leading to a significant performance improvement. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE) to illustrate the efficiency and the robustness of the proposed algorithm.

The ability to generate acoustic longitudinal and shear bulk waves in solid materials using a single transducer is a suitable feature required in several devices and systems involving ultrasonic waves. The problem addressed here is that of the, possibly wideband, multimode buffer delay line, usable, for example, in non-destructive evaluation applications. The solution considered for this purpose is to use rotated, Y-cut, single-layer or two-layer transducers. The theory pertinent to this kind of transducer is briefly recalled and the results of the numerical analysis using two different delay line materials and three different piezoelectric materials are reported in detail. One experimental realization is also described.

In this paper, we address the problem of OFDM channel estimation in the presence of phase noise (PHiN) and carrier frequency offset (CFO). For OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem. Here, we propose the joint estimation of channel impulse response (CIR), CFO and PHN with no prior statistical knowledge of PHN and SNR. The proposed approach uses a training OFDM symbol to track and estimate these many unknowns in the time domain by particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and the hybrid importance function which encompasses the advantages of both the optimal and the prior importance functions. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.