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).

Set membership adaptive filtering is known for a number of attractive features, including reduction of computational complexity due to less frequent coefficient updates. This paper addresses the problem of choosing the error bound to be used in SM adaptation algorithms. This choice has often been based on the experience of the designer, and affects directly not only algorithm performance, but also its computational complexity. We propose a time-varying error bound for a set-membership normalized least mean squares (SM-NLMS) algorithm that yields near constant average coefficient updating rate during both transient and steady state. The expressions given herein not only offer a method to calculate the error bound automatically, but also describes the behavior of a conventional SM-NLMS algorithm. The results were obtained for the particular application of linear time-invariant channel estimation, but certainly provide insightful hints to other scenarios

The performance of wireless communications systems can be significantly improved using the modulation diversity technique, which is basically based on the combination of a suitable choice of the reference angle of an MPSK constellation with independent interleaving of the symbol components. This technique presents good performance assuming the absence of estimation errors for channels characterized by flat fading. In this paper, the performance of this technique is analyzed taking into account the effects of channel estimation errors. It is shown, by simulation, that the efficiency of this technique is maintained even under this assumption. Additionally, the impact of the Doppler effect on the system performance is treated and a tradeoff between the interleaving depth and the error probability is achieved.

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.

This paper addresses the issue of blind adaptive maximum likelihood sequence estimation (BMLSE) using per survivor processing (PSP) over a fast frequency selective fading (FFSF) channel. Special care is dedicated to the choice of the adaptive filtering (AF) algorithm used and its influence on the receiver symbol error rate (SER) performance. An in-depth investigation of least mean square (LMS) and Kalman filtering (KF) performance is attempted. Regarding the KF algorithm two channel models were considered: first-order autoregressive (AR1) and second-order autoregressive (AR2) models. Three reception BMLSE schemes are discussed: BPSP-LMS, which uses LMS, and BPSP-KF1 and BPSP-KF2, which employ KF, according to the channel model used. For all the simulations accomplished in this work, the M-algorithm was used to obtain the survivors path. Results of several simulations, under varying signal-to-noise ratio (SNR) conditions and maximum Doppler shift (f/sub D/), are reported.

Intersymbol interference and additive noise are two of the main factors that limit the system communication performance for frequency selective channels. In particular, for applications over fast fading channels and blind reception, the noise level imposes severe performance degradation. This paper proposes the use of denoising techniques at the receiving end to reduce the noise level. The proposed technique does not require knowledge of the channel impulse response, being therefore suitable for blind equalization and mobile communications. The simulation accomplished in this work indicates that the proposed technique presents better performance than the one obtained with matched transmission and reception filters.