Charles Casimiro Cavalcante

Multiuser and Multirelay Cognitive Radio Networks Under Spectrum-Sharing Constraints

In this paper, the outage behavior of dual-hop multiuser multirelay cognitive radio networks under spectrum-sharing constraints is investigated. In the proposed cognitive radio network, the secondary network is composed of one secondary-user (SU) source that communicates with one out of L destinations through a direct link and also via the help of one out of N relays by using an efficient relay-destination selection scheme. Additionally, a selection combining (SC) scheme to select the best link (direct or dual-hop link) from the SU source is employed at the selected SU destination. Adopting an underlay approach, the SU communication is performed accounting for an interference constraint, where the overall transmit power is governed by the interference at the primary-user (PU) receiver, as well as by the maximum transmission power available at the respective nodes. Closed-form expressions for the outage probability are derived, from which an asymptotic analysis reveals that the diversity order of the considered system is not affected by the interference and is equal to N + L for both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols. The analytical results are corroborated by Monte Carlo simulations, and insightful discussions are provided.

Unsupervised Signal Processing : Channel Equalization and Source Separation

Introduction Channel Equalization Source Separation Organization and Contents Statistical Characterization of Signals and Systems Signals and Systems Digital Signal Processing Probability Theory and Randomness Stochastic Processes Estimation Theory Linear Optimal and Adaptive Filtering Supervised Linear Filtering Wiener Filtering The Steepest-Descent Algorithm The Least Mean Square Algorithm The Method of Least Squares A Few Remarks Concerning Structural Extensions Linear Filtering without a Reference Signal Linear Prediction Revisited Unsupervised Channel Equalization The Unsupervised Deconvolution Problem Fundamental Theorems Bussgang Algorithms The Shalvi-Weinstein Algorithm The Super-Exponential Algorithm Analysis of the Equilibrium Solutions of Unsupervised Criteria Relationships between Equalization Criteria Unsupervised Multichannel Equalization Systems withMultiple Inputs and/orMultiple Outputs SIMO Channel Equalization Methods for Blind SIMO Equalization MIMO Channels and Multiuser Processing Blind Source Separation The Problem of Blind Source Separation Independent Component Analysis Algorithms for Independent Component Analysis Other Approaches for Blind Source Separation Convolutive Mixtures Nonlinear Mixtures Nonlinear Filtering and Machine Learning Decision-Feedback Equalizers Volterra Filters Equalization as a Classification Task Artificial Neural Network Bio-Inspired Optimization Methods Why Bio-Inspired Computing? Genetic Algorithms Artificial Immune Systems Particle Swarm Optimization Appendix A: Some Properties of the Correlation Matrix Appendix B: Kalman Filter References Index

Tensor-Based Space-Time Multiplexing Codes for MIMO-OFDM Systems with Blind Detection

A new approach to space-time-frequency coding for multiple-input multiple-output (MIMO) systems based on orthogonal frequency division multiplexing (OFDM) is presented. Tensor-based space-time-multiplexing (TSTM) codes combine multi-stream spatial multiplexing and transmit diversity, and are based on a tensor modeling of the transmitted/received signals. The proposed codes are designed to offer some transmission flexibility by allowing a simple multiplexing-diversity-rate control as well as to achieve full space and multipath diversities in a frequency-selective channel. We show that the received signal has a tensor structure and this tensor modeling is exploited for blind separation/decoding of the transmitted information. Simulation results illustrate the performance of some TSTM codes with blind detection

Wavelet transform and artificial neural networks applied to voice disorders identification

The amount of non-invasive methods of diagnosis has increased due to the need for simple, quick and painless tests. Due to the growth of technology that provides the means for extraction and signal processing, new analytical methods have been developed to understand the complexity of the voice signals. This paper presents a new idea to characterize signals of healthy and pathological voice based on two mathematical tools widely known in the literature, Wavelet Transform (WT) and Artificial Neural Networks. Four classes of samples were used: one from healthy individuals and three from people with vocal fold nodules, Reinkes edema and neurological dysphonia. All the samples were recorded using the vowel /a/ in Brazilian Portuguese. The work shows that the proposed approach using WT is a suitable technique to discriminate between healthy and pathological voices.

Performance Analysis of Partial Relay Selection in Cooperative Spectrum Sharing Systems

In recent years, cooperative diversity and cognitive radio have received considerable attention from the wireless communications community due to its performance gains and spectrum utilization improvements, respectively, when compared to the traditional communications techniques. In one hand, cooperative diversity combats the signal fading caused by the multipath propagation. On the other hand, cognitive radio offers an efficient way to enhance spectrum utilization. However, these two promising technologies have been usually studied apart. In this paper, motivated by the important benefits of cooperative communications, we introduce decode-and-forward relays in primary/secondary spectrum sharing systems with the aim to provide a higher performance for the secondary user. Our analysis considers a partial relay selection in the first-hop transmission, with the relay nodes pertaining to the selected cluster positioned relatively close together (location-based clustering). The end-to-end performance of the secondary communication is investigated for several number of fading channels, such as Generalized Gamma (or α − μ), Nakagami-m, and Rayleigh. In particular, considering Rayleigh fading channels, closed-form expressions for the average bit error rate and outage probability are derived. Illustrative numerical examples are provided and the influence of the number of relays, fading parameters, and noise power imbalance between the hops on system performance is discussed.

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.

Quantization and noise impact over feedback reduction of MIMO systems using compressive sensing

In this paper we analyze spatially multiplexed MIMO systems with limited Channel State Information (CSI) and zero forcing (ZF) linear signal detection technique. Two schemes were considered: Quantization Codebook (QC) and Compressive Sensing (CS). Compressive Sensing is used to generate a reduced CSI feedback to the transmitter in order to reduce feedback load into the system. Performance of the schemes are compared by computational simulations of bit error rate (BER) curves for the considered approaches QC and CS. Furthermore, the presence of quantization error and noise, in the feedback link, were also evaluated for CS scheme.

A distributed approach to precoder selection using factor graphs for wireless communication networks

This paper addresses distributed parameter coordination methods for wireless communication systems. This proposes a method based on a message-passing algorithm, namely min-sum algorithm, on factor graphs for the application of precoder selection. Two particular examples of precoder selection are considered: transmit antenna selection and beam selection. Evaluations on the potential of such an approach in a wireless communication network are provided, and its performance and convergence properties are compared with those of a baseline selfish/greedy approach. Simulation results for the precoder selection examples are presented and discussed, which show that the graph-based technique generally obtains gain in sum rate over the greedy approach at the cost of a larger message size. Besides, the proposed method usually reaches the global optima in an efficient manner. Methods of improving the rate of convergence of the graph-based distributed coordination technique and reducing its associated message size are therefore important topics for wireless communication networks.

Geometry Induced by a Generalization of Rényi Divergence

In this paper, we propose a generalization of Renyi divergence, and then we investigate its induced geometry. This generalization is given in terms of a φ-function, the same function that is used in the definition of non-parametric φ-families. The properties of φ-functions proved to be crucial in the generalization of Renyi divergence. Assuming appropriate conditions, we verify that the generalized Renyi divergence reduces, in a limiting case, to the φ-divergence. In generalized statistical manifold, the φ-divergence induces a pair of dual connections D ( − 1 ) and D ( 1 ) . We show that the family of connections D ( α ) induced by the generalization of Renyi divergence satisfies the relation D ( α ) = 1 − α 2 D ( − 1 ) + 1 + α 2 D ( 1 ) , with α ∈ [ − 1 , 1 ] .

A graph-based approach for distributed parameter coordination in wireless communication networks

This paper addresses distributed parameters coordination methods for wireless communication systems founded on message-passing algorithms on graphs. This work provides evaluations on the potential of such an approach in a wireless communication network, and compares its performance and convergence properties with those of a baseline selfish/greedy approach. Simulation results for an example application, i.e. frequency reuse planning, is presented and discussed. The results show that graph-based techniques generally have higher probability of reaching (near) optimal solution than the greedy approach, though its rate of convergence tends to be slower and the message size is typically larger. Methods of improving the rate of convergence of graph-based distributed coordination techniques and reducing the associated message size are therefore important topics for future studies.