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See detailOversampled DFT-Modulated Biorthogonal Filter Banks: Perfect Reconstruction Designs and Multiplierless Approximations
Alves Martins, Wallace UL; Shankar, Bhavani UL; Ottersten, Björn UL

in IEEE Transactions on Circuits and Systems. II, Express Briefs (in press)

We propose a novel methodology for designing oversampled discrete Fourier transform-modulated uniform filter banks. The analysis prototype is designed as a Nyquist filter, whereas the synthesis prototype ... [more ▼]

We propose a novel methodology for designing oversampled discrete Fourier transform-modulated uniform filter banks. The analysis prototype is designed as a Nyquist filter, whereas the synthesis prototype is designed to guarantee perfect reconstruction (PR) considering oversampling. The resulting optimization problem fits into the disciplined convex programming framework, as long as some convex objective function is employed, as the minimization of either the stop-band energy or the maximum deviation from a desired response. The methodology also accounts for near-PR multiplierless approximations of the prototype analysis and synthesis filters, whose coefficients are obtained in the sum-of-power-of-two (SOPOT) space. The quantized coefficients are computed using successive approximation of vectors, which is able to yield filters with a reduced number of SOPOT coefficients in a computationally efficient manner. The proposed methodology is especially appealing for supporting actual hardware deployments, such as modern digital transparent processors to be used in next-generation satellite payloads. [less ▲]

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See detail'Faster-than-Nyquist Signaling via Spatiotemporal Symbol-Level Precoding for Multi-User MISO Redundant Transmissions
Alves Martins, Wallace UL; Spano, Danilo UL; Chatzinotas, Symeon UL et al

in International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2020), Barcelona 4-8 May 2020 (2020, May)

This paper tackles the problem of both multi-user and intersymbol interference stemming from co-channel users transmitting at a faster-than-Nyquist (FTN) rate in multi-antenna downlink transmissions. We ... [more ▼]

This paper tackles the problem of both multi-user and intersymbol interference stemming from co-channel users transmitting at a faster-than-Nyquist (FTN) rate in multi-antenna downlink transmissions. We propose a framework for redundant block-based symbol-level precoders enabling the trade-off between constructive and destructive multi-user and interblock interference (IBI) effects at the single-antenna user terminals. Redundant elements are added as guard interval to handle IBI destructive effects. It is shown that, within this framework, accelerating the transmissions via FTN signaling improves the error-free spectral efficiency, up to a certain acceleration factor beyond which the transmitted information cannot be perfectly recovered by linear filtering followed by sampling. Simulation results corroborate that the proposed spatiotemporal symbol-level precoding can change the amount of added redundancy from zero (full IBI) to half (IBI-free) the equivalent channel order, so as to achieve a target balance between spectral and energy efficiencies. [less ▲]

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See detailMultichannel Source Separation Using Time-Deconvolutive CNMF
Dias, Thadeu; Alves Martins, Wallace UL; Biscainho, Luiz Wagner

in Journal of Communication and Information Systems (2020), 35(1), 103-112

This paper addresses the separation of audio sources from convolutive mixtures captured by a microphone array. We approach the problem using complex-valued non-negative matrix factorization (CNMF), and ... [more ▼]

This paper addresses the separation of audio sources from convolutive mixtures captured by a microphone array. We approach the problem using complex-valued non-negative matrix factorization (CNMF), and extend previous works by tailoring advanced (single-channel) NMF models, such as the deconvolutive NMF, to the multichannel factorization setup. Further, a sparsity-promoting scheme is proposed so that the underlying estimated parameters better fit the time-frequency properties inherent in some audio sources. The proposed parameter estimation framework is compatible with previous related works, and can be thought of as a step toward a more general method. We evaluate the resulting separation accuracy using a simulated acoustic scenario, and the tests confirm that the proposed algorithm provides superior separation quality when compared to a state-of-the-art benchmark. Finally, an analysis of the effects of the introduced regularization term shows that the solution is in fact steered toward a sparser representation. [less ▲]

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See detailIntersymbol and Intercarrier Interference in OFDM Transmissions Through Highly Dispersive Channels
Alves Martins, Wallace UL; Cruz-Roldán, Fernando; Moonen, Marc et al

in Proc. of the 27th European Signal Processing Conference (EUSIPCO-2019) (2019, September)

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See detailTime-Deconvolutive CNMF for Multichannel Blind Source Separation
Dias, Thadeu; Biscainho, Luiz Wagner; Alves Martins, Wallace UL

in Anais do XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT 2019) (2019, September)

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See detailOn the Use of Vertex-Frequency Analysis for Anomaly Detection in Graph Signals
Lewenfus, Gabriela; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in Anais do XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT 2019) (2019, September)

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See detailSemi-blind Data-Selective and Multiple Threshold Volterra Adaptive Filtering
Barboza da Silva, Felipe; Alves Martins, Wallace UL

in Circuits, Systems, and Signal Processing (2019)

This work proposes the use of data-selective semi-blind schemes in order to decrease the amount of data used to train the adaptive filters that employ Volterra series, while reducing its computational ... [more ▼]

This work proposes the use of data-selective semi-blind schemes in order to decrease the amount of data used to train the adaptive filters that employ Volterra series, while reducing its computational complexity. It is also proposed a data-selective technique that exploits the structure of Volterra series, employing a different filter for each of its kernels. The parameter vector of these filters grows as the order of the kernel increases. Therefore, by assigning larger error thresholds to higher-order filters, it is possible to decrease their update rates, thus reducing the overall computational complexity. Results in an equalization setup indicate that both proposals are capable of achieving promising results in terms of mean square error and bit error rate at low computational complexity, and in the case of semi-blind algorithms, using much less training data. [less ▲]

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See detailConvex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms
Nagashima Ferreira, Tadeu; Alves Martins, Wallace UL; Lima, Markus et al

in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019, May)

Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both ... [more ▼]

Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might some- times lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one. [less ▲]

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See detailAchievable Data Rate of DCT-based Multicarrier Modulation Systems
Cruz-Roldán, Fernando; Alves Martins, Wallace UL; Sergio Ramirez Diniz, Paulo et al

in IEEE Transactions on Wireless Communications (2019), 18(3), 1739-1749

This paper aims at studying the achievable data rate of discrete cosine transform (DCT)-based multicarrier modulation (MCM) systems. To this end, a general formulation is presented for the full ... [more ▼]

This paper aims at studying the achievable data rate of discrete cosine transform (DCT)-based multicarrier modulation (MCM) systems. To this end, a general formulation is presented for the full transmission/reception process of data in Type-II even DCT and Type-IV even DCT-based systems. The paper focuses on the use of symmetric extension (SE) and zero padding (ZP) as redundancy methods. Furthermore, three cases related to the channel order and the length of the redundancy are studied. In the first case, the channel order is less than or equal to the length of the redundancy. In the second and third cases, the channel order is greater than the length of the redundancy; the interference caused by the channel impulse response is calculated, and theoretical expressions for their powers are derived. These expressions allow studying the achievable data rate of DCT-based MCM systems, besides enabling the comparison with the conventional MCM based on the discrete Fourier Transform. [less ▲]

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See detailNormalized LMS Algorithm and Data-selective Strategies for Adaptive Graph Signal Estimation
Jorge Mendes Spelta, Marcelo; Alves Martins, Wallace UL

in Signal Processing (2019)

This work proposes a normalized least-mean-squares (NLMS) algorithm for online estimation of bandlimited graph signals (GS) using a reduced number of noisy measurements. As in the classical adaptive ... [more ▼]

This work proposes a normalized least-mean-squares (NLMS) algorithm for online estimation of bandlimited graph signals (GS) using a reduced number of noisy measurements. As in the classical adaptive filtering framework, the resulting GS estimation technique converges faster than the least-mean-squares (LMS) algorithm while being less complex than the recursive least-squares (RLS) algorithm, both recently recast as adaptive estimation strategies for the GS framework. Detailed steady-state mean-squared error and deviation analyses are provided for the proposed NLMS algorithm, and are also employed to complement previous analyses on the LMS and RLS algorithms. Additionally, two different time-domain data-selective (DS) strategies are proposed to reduce the overall computational complexity by only performing updates when the input signal brings enough innovation. The parameter setting of the algorithms is performed based on the analysis of these DS strategies, and closed formulas are derived for an accurate evaluation of the update probability when using different adaptive algorithms. The theoretical results predicted in this work are corroborated with high accuracy by numerical simulations. [less ▲]

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