Butterfly Neural Filter Applied to Beamforming

Sousa, Tiago; Fernandes, Marcelo A. C.

doi:10.1109/access.2019.2929590

Cited by 6 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, except for the deadlock freedom property, data consistency and soundness are also important aspects of concurrent systems [67][68][69][70][71][72][73]. [44,74,75] have proposed several Petri net unfolding-based methods to verify these properties, including verifying CTL (Computation Tree Logic), detecting data inconsistency, and checking soundness of workflow systems.…”

Section: Discussionmentioning

confidence: 99%

Petri Net Unfolding-Based Detection and Replay of Program Deadlocks

Lu,

Lv,

Cui

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Program deadlock detection plays an important role in ensuring the stability of software systems. In view of the high efficiency and low false alarm rate, dynamic deadlock analysis methods are widely studied. However, existing dynamic analysis methods use lock graph or its extended variants to model program behavior. Since there are information loss in existing lock graphs, deadlock false positives still exist. To solve the problem, a novel 2-stage dynamic deadlock analysis method is proposed. In the first stage, a Petri net model of the program is constructed by analyzing program running traces. Then, unfolding-based Petri net dead marking detection methods are used to identify potential program deadlocks. In the second stage, based on Petri net unfolding techniques, a deterministic program scheduling scheme that can be used for deadlock replay is derived for each potential deadlock. Each successfully replayed program deadlock is a real deadlock. It is shown that the proposed deadlock detection and replay method can eliminate more false positives compared to general dynamic methods, and the deadlock replay scheme is deterministic and easy to understand compared with existing random scheduling strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

Petri Net Unfolding-Based Detection and Replay of Program Deadlocks

Lu,

Lv,

Cui

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Along with the application of nonlinear filters designed for specific problems in telecommunications, artificial neural networks (ANNs) have been extensively studied in various challenging areas of digital communications, including soft and hard fault detection, channel estimation, equalization, and beamforming [30][31][32][33][34][35][36][37][38][39][40]. Neural networks can operate like nonlinear filters, in a structure that can be modeled by nonlinear activation functions, as in multilayer perceptrons (MLPs), or by Gaussian neurons in radial basis function neural networks (RBFNN) [35].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Soares

Mayer

Castro

et al. 2021

Sensors

View full text Add to dashboard Cite

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

show abstract

“…However, some engineering problems are intrinsically dependent on complex-valued signals (e.g., channel equalization and beamforming). In order to circumvent this limitation, ANN algorithms based on complex numbers have already been proposed for some applications, such as channel equalization [5][6][7] and adaptive beamforming for wireless receivers [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…For beamforming at the receiver, ANN architectures such as the bi-dimensional neural beamformer with joint error (BNB-JE), the butterfly neural beamformer (NB-Butterfly), and the beamformer neural network (BNN) are potential algorithms for improving receiver performance [9][10][11]. On the other hand, beamforming for efficient transmission is necessary to increase spectral and energy efficiencies in some configurations of the next-generation wireless systems [15].…”

Section: Introductionmentioning

confidence: 99%

“…In such context, this article proposes a novel architecture of RBFNN, based on a complex multiple-input multiple-output (MIMO) RBFNN (CMM-RBF) for beamforming transmitters, in contrast to [9][10][11] which are designed to beamforming receivers. The proposed system applies a MIMO variation of the multiple-input single-output (MISO) phase transmittance RBFNN (PTRBFNN) [7] to a beamforming structure to generate a unified nonlinear solution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

Mayer

Soares

Arantes

2020

Sensors

View full text Add to dashboard Cite

The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the next-generation wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multiple-input multiple-output radial basis function neural network (CMM-RBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMM-RBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2D-grid array. Simulation results show that the proposed solution presents lower steady-state mean squared error, faster convergence rate and enhanced half-power beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario.

show abstract

Butterfly Neural Filter Applied to Beamforming

Cited by 6 publications

References 18 publications

Petri Net Unfolding-Based Detection and Replay of Program Deadlocks

Petri Net Unfolding-Based Detection and Replay of Program Deadlocks

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

Contact Info

Product

Resources

About