2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications 2011
DOI: 10.1109/trustcom.2011.256
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Comparative Analysis of Power Consumption in the Implementation of Arithmetic Algorithms

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Cited by 4 publications
(2 citation statements)
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“…After obtaining the filter size, it is possible to estimate the computational complexity associated with CD compensation. Our estimation is based on the number of real multiplications, which is the dominating parameter [32]. We focus on frequency-domain equalization (FDE), because of its computational efficiency in comparison with time-domain equalization for a sufficient filter length [28] [33].…”
Section: Computational Complexity Analysismentioning
confidence: 99%
“…After obtaining the filter size, it is possible to estimate the computational complexity associated with CD compensation. Our estimation is based on the number of real multiplications, which is the dominating parameter [32]. We focus on frequency-domain equalization (FDE), because of its computational efficiency in comparison with time-domain equalization for a sufficient filter length [28] [33].…”
Section: Computational Complexity Analysismentioning
confidence: 99%
“…per transmitted bit as the figure of merit, since multiplication is the dominant power consuming hardware-implemented operation, in comparison with addition and storage[113]. It is well known that thanks to the fast Fourier transform (FFT) algorithm, signal convolution with a sufficiently long FIR filter is more efficiently implemented in the frequency domain, using a data overlapping method (either overlap-&-save, or overlap-&-add)[50].…”
mentioning
confidence: 99%