Abstract
We consider blind equalization for block transmissions over the frequency selective Rayleigh fading channel. In the absence of pilot symbols, the receiver must be able to perform joint equalization and blind channel identification. Relying on a mixed discretecontinuous state-space representation of the communication system, we introduce a blind Bayesian equalization algorithm based on a Gaussian mixture parameterization of the a posteriori probability density function (pdf) of the transmitted data and the channel. The performances of the proposed algorithm are compared with existing blind equalization techniques using numerical simulations for quasi-static and time-varying frequency selective wireless channels.
Highlights
Blind equalization has attracted considerable attention in the communication literature over the last three decades
Relying on a mixed discretecontinuous state-space representation of the communication system, we introduce a blind Bayesian equalization algorithm based on a Gaussian mixture parameterization of the a posteriori probability density function of the transmitted data and the channel
Under the minimum mean square error (MMSE) criterion, the forward filtered channel vector estimated at instant k is obtained by marginalizing out the intersymbol interference (ISI) state variable xk|k =
Summary
Blind equalization has attracted considerable attention in the communication literature over the last three decades. The pioneering blind equalizers proposed by Sato [1] and Godard [2] use low-complexity finite impulse response filters These methods suffer from local and slow convergence and may fail on ill-conditioned or time-varying channels. The aforementioned methods employ a trellis description of the intersymbol interference (ISI) [7], where each discrete ISI state has its associated channel estimate Another fixed interval method, based on expectation-maximization channel identification, has appeared recently [8], but this technique is restricted to static channels. The main technical contribution of this work is the introduction of a blind equalization technique based on Gaussian mixtures. The operator det(·) will denote the determinant of a matrix
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