Cristiano Panazio

Channel tracking using particle filtering in unresolvable multipath environments

We propose a new timing error detector for timing tracking loops inside the Rake receiver in spread spectrum systems. Based on a particle filter, this timing error detector jointly tracks the delays of each path of the frequency-selective channels. Instead of using a conventional channel estimator, we have introduced a joint time delay and channel estimator with almost no additional computational complexity. The proposed scheme avoids the drawback of the classical early-late gate detector which is not able to separate closely spaced paths. Simulation results show that the proposed detectors outperform the conventional early-late gate detector in indoor scenarios.

Unsupervised channel equalization using fuzzy prediction-error filters

Ee present a new paradigm for unsupervised nonlinear equalization based on prediction-error fuzzy filters. Tests in different linear channel scenarios are carried out in order to assess the performance of the equalizer. The results show that the proposal is solid and may provide a performance close to that of a Bayesian equalizer.

A Class of Channels Resulting in Ill-Convergence for CMA in Decision Feedback Equalizers

This paper analyzes the convergence of the constant modulus algorithm (CMA) in a decision feedback equalizer using only a feedback filter. Several works had already observed that the CMA presented a better performance than decision directed algorithm in the adaptation of the decision feedback equalizer, but theoretical analysis always showed to be difficult specially due to the analytical difficulties presented by the constant modulus criterion. In this paper, we surmount such obstacle by using a recent result concerning the CM analysis, first obtained in a linear finite impulse response context with the objective of comparing its solutions to the ones obtained through the Wiener criterion. The theoretical analysis presented here confirms the robustness of the CMA when applied to the adaptation of the decision feedback equalizer and also defines a class of channels for which the algorithm will suffer from ill-convergence when initialized at the origin.

Cooperative spectrum sensing under unreliable reporting channels

This article analyzes a cooperative spectrum sensing scheme using a distributed approach with a fusion center considering an unreliable reporting channel. The spectrum sensing is applied to a cognitive radio system, where each cognitive radio sends its decision to a fusion center through a reporting channel, in which an n-out-of-K rule is applied. We compare the performance of the decision rules, analyzing the impact of the errors introduced by the reporting channel, considering the Bayes risk criterion.

On the frequency domain approach for spread spectrum receivers: towards a convergence of DS-CDMA, MC-CDMA and OFDM

The RAKE receiver is suboptimal when noise is non white, as in wideband CDMA systems. Frequency domain processing provides an efficient way to eliminate narrowband interference or exploit colored noise and it can significantly outperform the RAKE receiver. Additionally, other functions can be carried out directly in frequency domain, such as despreading, synchronization that is crucial for performance and equalization, which can mitigate multiuser interference. We discuss the frequency domain processing for DS-CDMA and potential gains are assessed by simulation results. An additional and important feature is that the frequency domain receiver for DS-CDMA also provides a common platform for MC-CDMA and OFDM, paving the way for an universal receiver.

Immune-inspired Dynamic Optimization for Blind Spatial Equalization in Undermodeled Channels

In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the dopt-aiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and, in all cases, its ability to find and track a solution close to the Wiener global optimum was attested. The obtained results reveal that the dopt-aiNet may decisively enhance the performance of adaptive arrays in scenarios built from elements that are representative of some aspects of real-world communication systems.

MLP-Based Equalization and Pre-Distortion Using an Artificial Immune Network

Due to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient-based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled

Space-time processing with a decoupled delayed decision-feedback sequence estimator

This work aims at investigating the performance of a decoupled space-time processing structure based on a delayed decision-feedback sequence estimator (D-ST-DDFSE), in the context of the Enhanced Data rates for GSM Evolution (EDGE) system. The main idea in using decoupled space-time (D-ST) processing structures is to separate co-channel interference (CCI) reduction from intersymbol interference (ISI) suppression. Consequently, all degrees of freedom of a space-time (ST) front-end are dedicated to treat CCI, leaving ISI to be suppressed by a temporal equalizer. Due to the 8-PSK modulation and the large delay spread values compared to the symbol period, optimum detection becomes too complex in the EDGE system, which makes DDFSE a promising scheme for ISI suppression. The performance of D-ST-DDFSE is analyzed through link-level simulations under the context of COST 259 channel models for typical urban (TU) and bad urban (BU) propagation scenarios. Improved performance of this D-ST technique over a space-time linear equalizer is observed.

Decoupled space-time processing: performance evaluation for a TDMA system

We investigate the performance of a decoupled space-time processing technique in a TDMA cellular system. This structure has, as its main characteristic, the possibility of giving more degrees of freedom to an antenna array, and it can thereby provide better cochannel interference cancellation. We analyze its performance by link-level simulations and its sensitivity for parameters like delay spread, path angle separation and signal-to-interference ratio. The results show that the decoupled space-time structure can outperform the conventional linear space-time structure, especially in cases with a high level of cochannel interference.

An uncoded BER comparison between DFE-SCCP and OFDM using a convex analysis framework

This article aims to establish a theoretical performance comparison between the Single Carrier with Cyclic Prefix (SCCP) equalized with a Decision Feedback Equalizer (DFE) and the Orthogonal Frequency Division Multiplexing (OFDM) systems. We consider an uncoded scenario and we derive analytical relations between these systemss bit error rate (BER). The comparison is accomplished for different modulation schemes.