Wallace Alves Martins

Energy Detection Technique for Adaptive Spectrum Sensing

The increasing scarcity in the available spectrum for wireless communication is one of the current bottlenecks impairing further deployment of services and coverage. The proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. This paper proposes a new strategy to detect adaptively white spaces in the radio spectrum. Such strategy works in cognitive radio (CR) networks whose nodes perform spectrum sensing based on energy detection in a cooperative way or not. The main novelty of the proposal is the use of a cost-function that depends upon a single parameter which, by itself, contains the aggregate information about the presence or absence of primary users. The detection of white spaces based on this parameter is able to improve significantly the deflection coefficient associated with the detector, as compared to other state-of-the-art algorithms. In fact, simulation results show that the proposed algorithm outperforms by far other competing algorithms. For example, our proposal can yield a probability of miss-detection 20 times smaller than that of an optimal soft-combiner solution in a cooperative setup with a predefined probability of false alarm of 0.1.

Sparsity-Aware Data-Selective Adaptive Filters

We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with data-selection mechanisms. Sparsity is promoted via some well-known nonconvex approximations to the l0 norm in order to increase convergence speed of the algorithms when dealing with sparse/compressible signals. These approximations circumvent some difficulties of working with the l0 norm, thus allowing the development of online data-selective algorithms. Data selection is implemented based on set-membership filtering, which yields robustness against noise and reduced computational burden. The proposed algorithms are analyzed in order to set properly their parameters to guarantee stability. In addition, we characterize their updating processes from a geometrical viewpoint. Simulation results show that the proposed algorithms outperform the state-of-the-art algorithms designed to exploit sparsity.

Affine projection algorithms for sparse system identification

We propose two versions of affine projection (AP) algorithms tailored for sparse system identification (SSI). Contrary to most adaptive filtering algorithms devised for SSI, which are based on the l1 norm, the proposed algorithms rely on homotopic l0 norm minimization, which has proven to yield better results in some practical contexts. The first proposal is obtained by direct minimization of the AP cost function with a penalty function based on the l0 norm of the coefficient vector, whereas the second algorithm is a simplified version of the first proposal. Simulation results are presented in order to evaluate the performance of the proposed algorithms considering three different homotopies to the l0 norm as well as competing algorithms.

Block-Based Transceivers With Minimum Redundancy

The standard design of multicarrier and single-carrier employing frequency-domain equalization transceivers requires, at least, L elements of redundancy, where L stands for the channel order. The redundancy eliminates the inherent interblock interference (IBI), which is part of all block-based transceivers, and turns the channel matrix circulant. The spectral decomposition of the circulant channel matrix through the discrete Fourier transform (DFT) allows the use of superfast algorithms for both the design of zero-forcing (ZF) and minimum mean squared error (MMSE) equalizers, and the equalization of received signals. However, it is well known that the minimum redundancy for IBI-free designs of block-based transceivers is [L/2] . This paper proposes practical ZF and MMSE solutions by using DFT, inverse DFT, and diagonal matrices. In particular, it is shown that, for some particular mild constraints on the channel model, the new designs may have similar bit error rate performance when compared to the standard ones, while keeping the same asymptotic complexity for the equalization process, that is, O(n log2 n) numerical operations. The key feature of the proposed transceivers is their higher throughput.

Suboptimal Linear MMSE Equalizers With Minimum Redundancy

Recent works have proposed practical zero-forcing (ZF) and linear minimum mean squared-error (LMMSE) solutions for fixed and memoryless block-based transceivers with minimum redundancy, using only half the amount of redundancy employed in standard systems. Their equalization processes require only O(M log2 M) operations. However, it may be difficult to apply LMMSE equalizers with minimum redundancy in some practical systems, given their higher number of operations. This letter proposes novel suboptimal LMMSE equalizers with minimum redundancy that require the same amount of computations of ZF equalizers, with a mild decrease in the throughput performance when compared to the optimal LMMSE solution.

Robust Acoustic Self-Localization of Mobile Devices

Self-localization of smart portable devices serves as foundation for several novel applications. This work proposes a set of algorithms that enable a mobile device to passively determine its position relative to a known reference with centimeter precision, based exclusively on the capture of acoustic signals emitted by controlled sources around it. The proposed techniques tackle typical practical issues such as reverberation, unknown speed of sound, line-of-sight obstruction, clock skew, and the need for asynchronous operation. After their theoretical developments and off-line simulations, the methods are assessed as real-time applications embedded into off-the-shelf mobile devices operating in real scenarios. When line of sight is available, position estimation errors are at most 4 cm using recorded signals.

Memoryless block transceivers with minimum redundancy based on Hartley transforms

This paper proposes real linear transceivers employing minimum redundancy, unlike the standard block transceivers that require, at least, L elements of redundancy, where L is the channel order. In all block-based systems, there is an inherent interblock interference (IBI) that can be eliminated by inserting redundancy. For transceivers based on the discrete Fourier transform (DFT), the redundancy induces a circulant channel matrix, allowing superfast implementations. Although it has been known for some time that the minimum redundancy for IBI-free designs of block transceivers is @?L/2@?, only recently practical DFT-based solutions using minimum redundancy were proposed. However, the extension of these solutions to real transforms, such as the discrete Hartley transform (DHT), is not straightforward. The only known solution imposes a symmetry on the channel model that is unlikely to be met in practice. This paper proposes transceivers with practical zero-forcing (ZF) and minimum mean-squared error (MMSE) receivers using DHT, diagonal, and antidiagonal matrices. The resulting systems are asymptotically as simple as orthogonal frequency-division multiplex (OFDM) and single-carrier with frequency-domain (SC-FD) equalization transceivers. In addition, they do not enforce constraints on the channel model. Several computer simulations indicate the higher throughput of the proposals as compared to the standard solutions.

Stability and MSE analyses of affine projection algorithms for sparse system identification

We analyze two algorithms, viz. the affine projection algorithm for sparse system identification (APA-SSI) and the quasi APA-SSI (QAPA-SSI), regarding their stability and steady-state mean-squared error (MSE). These algorithms exploit the sparsity of the involved signals through an approximation of the l0 norm. Such approach yields faster convergence and reduced steady-state MSE, as compared to algorithms that do not take the sparse nature of the signals into account. In addition, modeling sparsity via such approximation has been consistently verified to be superior to the widely used l1 norm in several scenarios. In this paper, we show how to properly set the parameters of the two aforementioned algorithms in order to guarantee convergence, and we derive closed-form theoretical expressions for their steady-state MSE. A key conclusion from the proposed analysis is that the MSE of these two algorithms is a monotonically decreasing function of the sparsity degree. Simulation results are used to validate the theoretical findings.

A Volumetric SRP with Refinement Step for Sound Source Localization

This paper proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the volumetric SRP (VSRP). As compared to the SRP, by deploying a sparser volumetric grid, the V-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of the location estimates. By appending a fine search step to the VSRP, its refined version (RV-SRP) improves on the compromise between complexity and accuracy. Experiments conducted in both simulatedand real-data scenarios demonstrate the benefits of the proposed approaches. Specifically, the RV-SRP is shown to outperform the SRP in accuracy at a computational cost of about ten times lower.This letter proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the refined volumetric SRP (RV-SRP). By deploying a sparser volumetric grid, the RV-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of location estimates. In addition, a refinement step improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios show that the RV-SRP outperforms state-of-the-art methods in accuracy with lower computational cost.

Open-source physical-layer simulator for LTE systems

This article describes a physical-layer simulator for both uplink and downlink connections of LTE systems, whose performances are assessed by simulating standardized environments. The simulator is compliant with Release 9 of LTE standard and it is publicly available for educational purposes, allowing students and researchers to test the performance of Signal Processing and Digital Communications techniques in an easy-to-use MATLAB framework. Users may benefit from implemented features such as channel estimation using different demodulation reference signals, channel coding, equalization, multiple access schemes in which multiple cells are employed, as well as diversity, spatial multiplexing, and beamforming transmissions. As an example, we evaluate the impact on the performance of an uplink connection due to inaccuracy in channel estimation and multi-user interference. In addition, we include the evaluation of using diversity and spatial multiplexing transmissions on downlink connections.