Ernesto Leite Pinto

Simple and robust analytically derived variable step-size least mean squares algorithm for channel estimation

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).

Analytical performance of the LMS algorithm on the estimation of wide sense stationary channels

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.

Using denoising at the receiver front-end for frequency-selective channels

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.

A simulation study of adaptive filtering applied to MLSE-PSP receivers

We address adaptive maximum-likelihood sequence estimation using per survivor processing (MLSE-PSP) over frequency selective fast fading channels. Special care is dedicated to the choice of the adaptive filtering algorithm (AFA) used in these schemes and its influence on the receiver bit error rate (BER) performance. We present an in-depth investigation of LMS and KF performance in the MLSE-PSP context. Regarding the KF algorithm, we focus attention on statistical channel models and propose the use of a second-order autoregressive (AR(2)) modeling as an alternative to the first-order autoregressive (AR(1)) modeling, which has been adopted in other works. The performance of the MLSE-PSP schemes is extensively evaluated by Monte Carlo simulation. Several experiments under conditions of varying signal-to-noise ratio (SNR), maximum Doppler shift (f/sub D/) and data block length (BL) are reported. The results presented indicate that the use of AR(2) produces remarkable performance improvement.

Performance of the LMS algorithm on the estimation of time-varying channels

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.

On the design of LMS-based channel estimators using the Doppler spread parameter

An analysis of the optimization of the LMS (Least Mean Square) algorithm for the estimation of time-varying and frequency-selective communication channels is here presented. In contrast to previous works on this subject, in which the step-size optimization is rooted on the assumption of specific channel models, this analysis is much more generic in respect to the modelling of both the Doppler and delay spreadings. Besides, it addresses not only the steady-state performance of channel estimators, but also their transient behaviors. Several useful approximate expressions for designing LMS-based channel estimators are herein derived and validated by comparisons with simulation and with a previous work on this kind of analysis. These expressions are remarkably suitable for practical application, since they depend on a few parameters of the communication system. With regard to the channel variability, the knowledge of the Doppler spread parameter is shown to be enough to optimize the performance of a LMS channel estimator using the analysis here presented.

Highly maneuverable target tracking based on efficient propagation of model hypotheses

This paper presents an algorithm based on the multiple model approach for tracking highly maneuverable targets. The proposed method aims at propagating a low number of model hypotheses while still yielding low estimation errors compared to other multiple model algorithms. Merging and pruning techniques are used to keep very small sets of model sequences matched with the target dynamics. Simulation results show that the proposed method is able to attain superior performance during periods of no maneuver compared to the Interacting Multiple Model algorithm as well as when abrupt changes take place in the target movement.

Detection of Equalization Errors in Time-Varying Channels

This article presents a new procedure to detect equalization errors in time-varying channels (TVC). This procedure has low computational complexity and can be used with different modulation or equalization schemes. In order to detect the occurrence of errors a test of hypothesis is performed using a proper measure of distance between decision-directed channel estimates produced by two adaptive filters with distinct characteristics of robustness to the use of erroneous references. Several results of performance evaluation by simulation are presented and indicate the effectiveness of the proposed scheme.

MLSE-PSP receiver with optimized LMS step-size parameter

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.

Bayesian blind turbo equaliser with reduced complexity based on channel estimate monitoring

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 studys 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.