This paper deals with the joint estimation of Rayleigh fading channels and symbols in a MC-DS-CDMA system. Formerly, particle filtering has been introduced as a set of promising methods to solve communication problems. PF consists in simulating possible values of the unkwnown parameters and selecting the most likely candidates with regard to the received signal. Here, the Rao-Blackwellized particle filter (RBPF) is used to significantly decrease the variance of the channel/symbol estimates. Our contribution is twofold. Firstly, sinusoidal stochastic models have been shown to better represent the statistical properties of Rayleigh channels than classical autoregressive models. Therefore, the proposed RBPF estimator is based on these models which are expressed as the sum of two sinusoids in quadrature at the maximum Doppler frequency with autoregressive processes as amplitudes. The model parameters are unknown and need to be estimated. Since PFs are not well-suited to recover non-varying parameters, we propose to cross-couple the RBPF with a Kalman filter which makes use of the RBPF ouputs to sequentially update the parameters. Secondly, the choice of an efficient proposal distribution to simulate the particles is crucial for PF performance. We suggest using a suboptimal distribution which simulates likely values of the symbols at a reasonable computational cost.

This letter deals with a new Rayleigh channel simulator. In many cases, the channel is modeled by an autoregressive process, but this choice is not well suited due to the band limitation of the theoretical Rayleigh channel power spectrum density. Therefore, we suggest using a low-pass filtered version of the so-called stochastic sinusoidal process. It consists of sinusoids in quadrature with random magnitudes modeled by autoregressive processes. Unlike the autoregressive channel modeling, this simulator has the advantage of exhibiting the power spectrum density (PSD) peaks at the maximum Doppler frequency. Since estimating the autoregressive parameters of the amplitudes directly using a matrix approach from the theoretical Rayleigh channel autocorrelation leads to numerical errors, we propose two alternative methods using either the asymptotic behavior of the Bessel function or genetic algorithms.

In this paper, we propose a new pilot-aided channel estimator. Among the existing approaches, some are based on adaptive algorithms, but they are outperformed by methods where the channel is modeled by an AR or an ARMA process. In that case, estimating the model parameters from noisy observations and selecting the model orders are challenging problems. To avoid them, we propose to view the channel estimation as a realization issue. By taking advantage of the subspace methods for identification, the proposed estimator provides the system matrices in the state-space representation of the channel directly from the output observations. At that stage, the channel process can be estimated using a Kalman filter. This method has the advantage of being non-iterative and avoiding an a priori model for the channel.

In this paper, we propose a new Rayleigh channel simulator. Modeling the channel by an AR process leads to numerical problems due to the bandlimitation of the theoretical power density spectrum (PSD) of a Rayleigh channel. Therefore, we suggest modeling the channel by a low-pass filtered version of the so-called stochastic sinusoidal process. It consists of sinusoids in quadrature with random magnitudes modeled as AR processes. To estimate the AR parameters of the amplitudes, we take advantage of the asymptotic behavior of the first-kind zero-order Bessel function. We show that unlike an AR channel modeling, this simulator has the advantage of exhibiting the PSD peaks at the maximum Doppler frequency, for any AR process order.