Murilo Bellezoni Loiola

Modified Kalman Filters for Channel Estimation in Orthogonal Space-Time Coded Systems

We derive and analyze two modified Kalman channel estimators (KCE) for time-varying, flat, spatially correlated MIMO channels in systems employing orthogonal space-time block codes: the steady-state KCE, which is less complex than the KCE, and the fading memory KCE, which is more robust to model mismatch.

Blind search for optimal Wiener equalizers using an artificial immune network model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.

Achievable rates of DSL with crosstalk cancellation

Crosstalk is one of the main limiting factors in the data rates achievable by digital subscriber line (DSL) systems, and several algorithms have been proposed to mitigate this impairment. In this paper, we compare the achievable rates of binders under different crosstalk-mitigating techniques. When computing these rates, we also compare two different power constraints: either on the total power in the binder or on the power in each twisted wire pair. We will see that, for the scenarios considered in this paper, the fact that the signals are jointly processed in one or both ends of the DSL link leads to roughly the same performance, which can be far superior to that of systems with no cooperation between the users. Both power constraints also lead to similar achievable rates. Copyright

Correlation and spectral properties of chaotic signals generated by a piecewise-linear map with multiple segments

In recent decades, many studies with practical applications of chaotic signals in telecommunications and signal processing have been carried out. Although spectral analysis is of relevance in these areas, there are few analytical results concerning the spectral characterization of chaotic signals. In this work, we deduce analytical expressions for autocorrelation sequence, power spectral density and essential bandwidth of chaotic signals generated by a piecewise-linear map, with multiple segments. We present numerical simulations to confirm the theoretical results. HighlightsWe deduce the power spectral density and bandwidth of signals generated by a piecewise-linear map, with multiple segments.Numerical simulations are presented to confirm the theoretical results.Results generalize particular cases presented in the literature and can be useful in the analysis and synthesis of chaos-based communication systems.

Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

In this paper we propose low-complexity algorithms for estimating flat, time-varying and spatially correlated MIMO channels. The proposed estimators employ Kalman filters to track the channel in orthogonal space-time block coded systems. After developing a state-space model for spatially correlated MIMO channels, we show that the proposed estimators can be simplified by using the orthogonality inherent to orthogonal space-time block codes. We also show that the channel estimates provided by the proposed algorithms correspond to weighted sums of instantaneous maximum likelihood channel estimates. For constant modulus signal constellations, we reduce the receiver complexity even more by proposing a steady-state Kalman filter. Simulation results indicate that there is no significant performance degradation of the steady-state filter compared to the first proposed algorithm and that both algorithms outperform conventional adaptive filters.

Turbo-Fuzzy Equalization for Single-Carrier Power Line Channels

This paper introduces a turbo-fuzzy equalizer (TFEQ) to cope with the inter-symbol interference effects and impulsive noise occurrences in bandlimited digital communication channels, such as the indoor and outdoor power line circuits. We exploit a close relationship between the fuzzy and Bayesian equalizers to derive an adaptive nonsingleton turbo-fuzzy equalization technique. Generally speaking, the TFEQ is obtained by introducing an adaptive decision feedback nonsingleton fuzzy equalizer into the turbo equalization structure. Simulation results reveal the applicability of this approach to overcome the noise impairments in the power line frequency range for broadband applications.

The capacity of binders for MIMO digital subscriber lines

Crosstalk is one of the main limiting factors in the data rates achievable by digital subscriber line (DSL) systems, and several algorithms have been proposed to mitigate this impairment. In this paper, we compare the capacity of binders under different crosstalk-mitigating techniques. When computing capacity, we also compare two different power constraints: either on the total power in the binder or on the power in each pair. We will see that, for the scenarios considered in this paper, the fact that the signals are jointly processed in one or both ends of the DSL link leads to roughly the same performance, which is far superior to that of systems with no cooperation between the users. Both power constraints also lead to similar achievable rates.

Spectral Properties of Chaotic Signals Generated by the Bernoulli Map

In the last decades, the use of chaotic signals as broadband carriers has been considered in Telecommunications. Despite the relevance of the frequency domain analysis in this field, there are few studies that are concerned with spectral properties of chaotic signals. Bearing this in mind, this paper aims the characterization of the power spectral density (PSD) of chaotic orbits generated by Bernoulli maps. We obtain analytic expressions for autocorrelation sequence, PSD and essential bandwidth for chaotic orbits generated by this map as function of the family parameter and Lyapunov exponent. Moreover, we verify that analytical expressions match numerical results. We conclude that the power of the generated orbits is concentrated in low frequencies for all parameters values. Besides, it is possible to obtain chaotic narrowband signals.

Denoising chaotic time series using an evolutionary state estimation approach

This work presents a method for denoising chaotic time series when the structure of the underlying dynamics is known, albeit not the associated initial conditions and parameters. The strategy relies on finding the initial conditions and free parameters that minimize deviations - in the mean-squared error sense - from the noisy observations, thus providing the means to identify the original model that engenders the noise-free chaotic signal. To accomplish this purpose, an evolutionary immune-inspired approach was adopted. The reason for choosing this approach was its significant global search potential and the fact that it does not demand cost function manipulations. The proposal can be applied to general contexts, but a most promising perspective is its use in communications systems employing chaotic signals, for which the existence of knowledge about the underlying dynamics is a reasonable assumption.

A state-space approach to semi-blind signal detection in fast frequency-selective fading MIMO channels

In this paper, we propose a semi-blind state-space based receiver that jointly performs channel estimation and data detection in MIMO systems subject to fast frequency-selective fading. To accomplish these two tasks, we first define state equations representing the dynamics of channel and transmitted signals. Then, we obtain the state vector by concatenating the transmitted signals and the channel coefficients. This choice of state vector leads to a nonlinear observation equation and hence to the use of the extended Kalman filter (EKF) to estimate the states variables. We then develop the EKF and show that the proposed receiver is a generalization of many similar receivers for SISO channels. We also develop a reduced complexity version of the proposed algorithm. Simulation results show the performance gains of the proposed receiver when compared to other commonly used receivers.