Extended Bennia-Riad criterion for iterative frequency-domain deconvolution

Dhaene, Tom; Martens, Z.; Zutter, D. De

doi:10.1109/19.293416

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…From Equation (3), there is no effect in the deconvolution error when λ is too low. On the other hand, the signal distortion is high if λ is too high [37]. A decision criterion was to choose a value of λ that produced a root mean square of the normalized error of 0.1.…”

Section: Extraction Of the Received Signalmentioning

confidence: 99%

Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions

Azevedo

Santos

2022

Electronics

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Many authors suggest directional antennas to enhance the transmission performance of ZigBee networks. For line-of-sight propagation, directional antennas can extend the transmission range or reduce the transmit power. Directional antennas may also reduce interference between networks operating in the same frequency channel. However, these antennas may not perform similarly under non-line-of-sight propagation conditions. This work presents a study with ZigBee modules comparing the performance of a directional antenna with an omnidirectional one. The measurements were conducted on a university campus for different propagation outdoor environments. A deconvolution technique was applied to estimate the received signal as a function of the azimuth angle. The results demonstrated that the received power followed the gain difference between antennas only for paths with low attenuation. Considering the same Effective Isotropic Radiated Power (EIRP), the system with directional antennas started to lose packets at the same distance as the omnidirectional antennas. The directional antenna did not allow the increase in the link range compared to the omnidirectional antenna. The power consumption was also measured for different transmit power levels of the ZigBee radio. The study showed that the control circuits of directional antennas typically consume more power than omnidirectional antennas operating at a higher transmit power level.

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Section: Extraction Of the Received Signalmentioning

confidence: 99%

Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions

Azevedo

Santos

2022

Electronics

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“…The form (20) illustrates the shrinkage of the SW filter w.r. In the complementary case |Z[k]| < 2, the shrinkage factor is strictly zero.…”

Section: "Self-wiener" Filteringmentioning

confidence: 99%

"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals

Weiss¹,

Nadler²

2020

Preprint

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We consider the fundamental problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequencydomain, where it tries to mimic the optimal unrealizable Wienerlike filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold value at each frequency is found in a fully datadriven manner. We provide an analytical performance analysis, and derive approximate closed-form expressions for the residual Mean Squared Error (MSE) of our proposed estimator in the low and high Signal-to-Noise Ratio (SNR) regimes. We show analytically that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the performance of the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.

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“…Deconvolution can be an unstable procedure when parts of the frequency-domain spectrum of the waveform to deconvolve have low amplitudes in comparison to the other frequency-domain components. Dhaene et al (1994) describe an adaptive filter to reduce many of the problems associated with frequency-domain deconvolution of time-domain data.…”

Section: Wave Plot Deconvolvementioning

confidence: 99%