Abstract-This article aims to establish a performance comparison between the single-carrier with cyclic prefix (SCCP) and the orthogonal frequency division multiplexing (OFDM) for frequency selective block fading channels without channel information on the transmitter side and using error correcting codes. For the SCCP scheme, it will be considered both linear and decision-feedback equalizers implementations. We will assess both schemes bit-error rates and capacities for different modulations and coding rates using different approaches. Firstly, analytical results are obtained for a convex analysis framework based on the OFDM effective signal-to-noise ratio and the cutoff rate together with the Shannon capacity analysis. In addition, Monte Carlo simulations are evaluated, corroborating previous analytical results and giving further insights on the comparison.

In this work we present a new paradigm for unsupervised nonlinear equalization based on prediction-error fuzzy filters. Tests i n different linear channel scenarios are carried out i n order t o assess the performance of t h e equalizer. T h e results show that the proposal is solid and may provide a performance close to that of a Bayesian equalizer. I N T R O D U C T I O NThe need for optimal performance and the continuous systemic refinement are the main reasons behind the growing interest in nonlinear equalization. This interest, together with advances in the field of computational intelligence and nonlinear filtering, account for a solid research corpus, which attests the relevance of the field.Usually, nonlinear equalizers are adapted with the aid of a pilot signal, i.e., in a supervised fashion. This is quite natural. since the usual test of structures and algorithms must be carried out in an environment as simple as possible. Furthermore, the assumption of supervised training is reasonable in some contexts and also gives rise to a more propitious scenario for optimality analysis.However, a general nonlinear filtering paradigm should not rely on supervised learning, since a reference signal may not be available in all cases. This is the motivation behind the proposal of unsupervised equalization criteria. Although criteria based on signal statistics work well on the adaptation of linear filters, it is not. certain that they will assure the correct adaptation of nonlinear filters. Ironically, this kind of problem arises exactly from the great approximation potential of nonlinear structures.Therefore, it becomes imperative to look for unsupervised equalization criteria adequate to the problem of nonlinear filtering. In particular Cavalcante et al. [l] demonstrated that a prediction approach can be effective 0-7803-8178-5/03/$17.00 Q 2003 IEEE 869

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This article aims to analyze a cooperative spectrum sensing scheme using a centralized approach with unreliable reporting channel. The spectrum sensing is applied to a cognitive radio system, where each cognitive radio performs a simple energy detection and send the decision to a fusion center through a reporting channel. When the decisions are available at the fusion center, a n-out-of-K rule is applied. The impact of the choice of the parameter n in the cognitive radio system performance is analyzed in the case where the reporting channel introduces errors.

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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

Resumo-Este artigo apresenta e discute importantes ferramentas da geoestatística utilizadas para a geração de mapas de ambiente de rádio (REM-Radio Environment Maps) em sistemas de comunicações sem fio. A partir de um processo de amostragem espacial, o método Kriging Ordinário (KO) foi utilizado para gerar o REM. Resultados de simulação mostram a influência de parâmetros relacionados à modelagem do ambiente de rádio na precisão de geração do REM. Sobre esta perspectiva, são apontados desafios e limitações relacionados à geração e uso do REM em aplicações de sistemas de comunicações sem fio.

In this article we analyze the diffusion normalized least mean square (NLMS) and its set-membership version (SM-NLMS) diffusion algorithms in a scenario where sensor nodes are subjected to different noise variances. We show through simulation that the SM-NLMS is a more robust algorithm in such condition, in addition to the provided reduced energy consumption. We also show that, in such context, the reduced feedback SM-NLMS (RF-SM-NLMS) presents a similar performance when compared to the SM-NLMS with an additional energy saving and lower channel occupancy. Moreover, we propose an adaptive way to choose the SM-NLMS and RF-SM-NLMS parameters in order to provide further performance enhancement in the presence of nodes subjected to different noise variances.