The authors recall the application of fixed‐lag particle smoothing (FLPS) to perform blind turbo equalisation under a Bayesian framework, assuming that the noise variance is unknown. This approach has been successfully applied in previous works, at the price of increasing equaliser computational complexity. This study's main contribution is to propose a strategy to monitor, along the iterations, the quality of channel estimate and soft information about the coded symbols, to switch from FLPS to the BCJR algorithm when performing soft‐input soft‐output equalisation. This approach allows saving significant computational effort without affecting bit error rate performance. Simulation results and a careful computational complexity analysis show the effectiveness of the techniques herein presented.

A new variable step-size least mean squares (VSS-LMS) algorithm for the estimation of frequency-selective communications channels is herein presented. In contrast to previous works, in which the step-size adaptation is based on the instantaneous samples of the error signal, this algorithm is derived on the basis of analytical minimisation of the ensemble-averaged mean-square weight error. A very simple rule for step-size adaptation is obtained, using a small number of communication system parameters. This is another significant difference from other proposals, in which a large number of control parameters should be tuned for proper use. The algorithm here proposed is shown to be applicable to both time-varying and time-invariant scenarios. While the lack of a termination rule for step-size adaptation is a common characteristic of other schemes, the algorithm here presented adopts a criterion for stopping the step-size adaptation that assures optimal steady-state performance and leads to large computational savings. A simulation-based performance comparison with other VSS-LMS schemes is provided, including their application to maximum likelihood sequence estimation receivers using per survivor processing (MLSE/PSP). The results show that the algorithm proposed in this work has good performance characteristics and a very low computational cost, specially in the application to MLSE/PSP receivers. Besides, this algorithm is shown to be robust to changes in the signal-to-noise ratio (SNR).

Since its advent, wavelet coding has been recognized as a promising technique for wireless transmission systems, especially due to its low decoding complexity and good performance over multipath fading. However, this technique requires unusual modulation schemes in order to be fully exploited. A BER analysis of wavelet-coded transmission systems over Rayleigh fading channels is presented in this work. This analysis is used in the design of novel signal constellations for wavelet-coded systems by means of numerical performance optimization. Numerical results obtained from analysis and simulations show that the wavelet-coded systems derived herein have very good performance characteristics over fading channels.

A novel methodology for obtaining statistics of a hidden Markov model (HMM) associated to a burst error process is presented which is based on the CCITT definition of a burst error. Its fundamental step is the construction of a properly chosen classical Markov chain associated to the HMM which is shown to give rise to the derivation of recursive expressions for the burst statistics of interest. This methodology is applied to derive several burst error statistics related to the CCITT definition of burst, which have been addressed by computer simulation in other works. Some numerical examples are presented and illustrate the effectiveness of the presented methodology in the derivation of expressions specially suitable for quick evaluation.

The performance of the least mean square (LMS) algorithm on the estimation of time-varying channels is analytically evaluated, using the estimation error correlation matrix, the mean-square weight error (MSWE) and the mean-square estimation error (MSE) as parameters. Expressions for those parameters are obtained from a set of hypotheses usually adopted in the communication systems context. The channel is modeled as a wide sense stationary (WSS) discrete time stochastic field with known autocorrelation. The expressions for the steady-state MSWE and MSE are particularized for the class of WSS channel models, and an original analysis of the optimum LMS step-size parameter for usual channel models is addressed. For the sake of comparison with other works, the analytical step-size optimization for random-walk models is also considered. Several estimates of MSWE curves obtained by computer simulation are compared with analytical results for validation purposes. A very good agreement between simulated and analytical results for both the MSWE expressions and the optimum value of the LMS step-size parameter is shown.

This paper proposes a new strategy for establishing the LMS step-size parameter in the context of adaptive MLSE-PSP reception over time-varying frequency-selective fading channels. This strategy is based on the optimization of the step-size in the sense of minimizing the LMS steady-state mean square error (MSE) in the estimation of the channel impulse (IR) response. Analytical expressions of the optimum step-size are derived assuming Doppler spectrum models usually adopted for mobile communication channels. These expressions are used to choose the step-size of LMS based MLSE-PSP receivers under different conditions of Doppler spread. Several computer simulation results are provided which show the performance benefits so obtained.

This article proposes the use of denoising techniques at the receiver front-end in order to combat the effects of additive noise in digital transmission over frequency-selective channels. The proposed technique does not require knowledge of the channel impulse response, and is, therefore, potentially suitable for application in several receiver structures, including those with blind equalizers and adaptive receivers for mobile communications.

The performance of the least-mean-square (LMS) algorithm on the estimation of time-varying channels is analytically evaluated, using the estimation error correlation matrix and the mean square error (MSE) as parameters. Expressions for those parameters are obtained from a set of hypotheses usually adopted in the communication systems context. The channel is modeled as a wide sense stationary discrete time stochastic field with known autocorrelation. The expression for the steady state MSE is particularized assuming wide sense stationary-uncorrelated scattering (WSS-US) channel model. Several estimates of MSE curves obtained by computer simulation are compared with analytical results for validation purposes. A very good agreement between simulated and analytical results is shown.

The design of symbol sequences to be used in quasi-analytical simulation to estimate performance parameters of digital transmission systems is considered. An efficient method is presented for including the effects of external interfering signals in the evaluation of error probabilities. It consists of using simulated signals to obtain the moments of interference samples and applying the Gauss quadrature rule to calculate error probabilities. Several results are presented that confirm both the accuracy and efficiency of this procedure in the simulation of some interference environments.>