Paulo Diniz | Universidade Federal do Rio de Janeiro (UFRJ) (original) (raw)
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Papers by Paulo Diniz
Cambridge University Press eBooks, Jun 15, 2012
Anais do XXII Simpósio Brasileiro de Telecomunicações
6th International Congress on Sound and Vibration, ICSV6, 1999
2018 26th European Signal Processing Conference (EUSIPCO), 2018
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for ... more In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l0-norm Recursive least-Squares (l0-RLS), in order to exploit the sparsity of an unknown system. The first algorithm, applies a discard function on the weight vector to disregard the coefficients close to zero during the update process. The second algorithm, employs the sparsity-promoting scheme via some non-convex approximations to the l0-norm. In addition, we consider the respective versions of these algorithms in data-selective versions in order to reduce the update rate. Simulation results show similar performance when comparing the proposed algorithms with standard Recursive Least-Squares (RLS) algorithm while the proposed algorithms require lower computational complexity.
2021 IEEE Statistical Signal Processing Workshop (SSP), 2021
Orthogonal frequency-division multiplexing (OFDM) systems have championed the elimination of inte... more Orthogonal frequency-division multiplexing (OFDM) systems have championed the elimination of inter-symbol interference (ISI) and inter-block interference (IBI) originated from multi-path fading. By introducing some redundant symbols at the transmitter such as zero padding (ZP), spectral efficiency is reduced. The amount of redundancy is related to the channel-model order, an information carrying some uncertainty in practical situations, particularly when one is willing to increase data transmission. The recent trend of utilizing neural networks to address some communication issues sparkled the idea of exploiting machine-learning (ML) to improve the performance of ZP-OFDM transceivers whenever the channel order is not known. This work presents a novel application of ML to address ZP-OFDM physical layer issues. The simulation results show that the ML ZP-OFDM brings about performance improvements, such as reduced bit-error-rate (BER), when the amount of redundancy is insufficient and some form of nonlinearity is present at the transmitter end.
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017
Anais do XXVI Simpósio Brasileiro de Telecomunicações, 2008
IEEE Transactions on Wireless Communications, 2019
IEEE Transactions on Circuits and Systems II: Express Briefs, 2017
Cambridge University Press eBooks, Jun 15, 2012
Cambridge University Press eBooks, Jun 15, 2012
Cambridge University Press eBooks, Jun 17, 2012
Cambridge University Press eBooks, Jun 17, 2012
Subband adaptive filtering has been studied by a large number of researchers. The main alternativ... more Subband adaptive filtering has been studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, that use local errors or global error in the adaptation algorithm. In this paper a theoretical convergence analysis of an oversampled subband adaptive filtering structure with global error is presented. The convergence rate of the adaptation algorithm can be estimated from the results of this analysis. Computer simulations are presented to illustrate the convergence behavior of the subband adaptive algorithm and to verify the theoretical results
Cambridge University Press eBooks, Jun 15, 2012
Anais do XXII Simpósio Brasileiro de Telecomunicações
6th International Congress on Sound and Vibration, ICSV6, 1999
2018 26th European Signal Processing Conference (EUSIPCO), 2018
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for ... more In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l0-norm Recursive least-Squares (l0-RLS), in order to exploit the sparsity of an unknown system. The first algorithm, applies a discard function on the weight vector to disregard the coefficients close to zero during the update process. The second algorithm, employs the sparsity-promoting scheme via some non-convex approximations to the l0-norm. In addition, we consider the respective versions of these algorithms in data-selective versions in order to reduce the update rate. Simulation results show similar performance when comparing the proposed algorithms with standard Recursive Least-Squares (RLS) algorithm while the proposed algorithms require lower computational complexity.
2021 IEEE Statistical Signal Processing Workshop (SSP), 2021
Orthogonal frequency-division multiplexing (OFDM) systems have championed the elimination of inte... more Orthogonal frequency-division multiplexing (OFDM) systems have championed the elimination of inter-symbol interference (ISI) and inter-block interference (IBI) originated from multi-path fading. By introducing some redundant symbols at the transmitter such as zero padding (ZP), spectral efficiency is reduced. The amount of redundancy is related to the channel-model order, an information carrying some uncertainty in practical situations, particularly when one is willing to increase data transmission. The recent trend of utilizing neural networks to address some communication issues sparkled the idea of exploiting machine-learning (ML) to improve the performance of ZP-OFDM transceivers whenever the channel order is not known. This work presents a novel application of ML to address ZP-OFDM physical layer issues. The simulation results show that the ML ZP-OFDM brings about performance improvements, such as reduced bit-error-rate (BER), when the amount of redundancy is insufficient and some form of nonlinearity is present at the transmitter end.
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017
Anais do XXVI Simpósio Brasileiro de Telecomunicações, 2008
IEEE Transactions on Wireless Communications, 2019
IEEE Transactions on Circuits and Systems II: Express Briefs, 2017
Cambridge University Press eBooks, Jun 15, 2012
Cambridge University Press eBooks, Jun 15, 2012
Cambridge University Press eBooks, Jun 17, 2012
Cambridge University Press eBooks, Jun 17, 2012
Subband adaptive filtering has been studied by a large number of researchers. The main alternativ... more Subband adaptive filtering has been studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, that use local errors or global error in the adaptation algorithm. In this paper a theoretical convergence analysis of an oversampled subband adaptive filtering structure with global error is presented. The convergence rate of the adaptation algorithm can be estimated from the results of this analysis. Computer simulations are presented to illustrate the convergence behavior of the subband adaptive algorithm and to verify the theoretical results