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This novel scheme is a SCM in nature and is incorporated with a minimum phase pre-filter and a simplified trellis-based equalizer at the receiver side.
The coefficients of minimum phase pre-filter could be obtained from a pseudo noise (PN) sequence-based channel estimation with very short training symbol length.
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The first 6 most significant taps are used to calculate the minimum-phase pre-filter.
In [22], the coefficients of the minimum-phase pre-filter are calculated by the linear prediction from the estimated CIR.
For the proposed PN-PAM transmission scheme, the first 13 prominent taps in the CIR estimate are used for the calculation of minimum-phase pre-filter.
The analysis of this minimum-phase pre-filter calculation shows that the overall computational complexity of linear prediction method is significantly lower than that of the minimum mean-squared error (MMSE -DFE MMSE -DFE2].
In addition, the system overall non-linearity causes multi-path delay spreading, which is shown as the additional delay path at 9th and 10th taps in Fig. 7. Based on the CIR, we can calculate the coefficients of the minimum-phase pre-filter using (5).
With the help of the PN based channel estimation, minimum-phase pre-filter and reduced-state sequence estimation based equalizer, the proposed PAM transmission scheme can significantly reduce the training overhead for channel estimation in the classical PAM systems using decision feedback equalizer (DFE).
Linear phase FIR filters and minimum phase IIR filters fulfilling the class 1 requirements of the IEC 61260 standard have been designed, and their errors compared.
The new class of FIR filters lays in between the exact linear phase filters at all frequencies and the minimum phase FIR filters based on the amplitude approximation only.
Reference [10] used the discrete Hilbert transform as minimum phase type filter to forecast wind speed and identify the characterization of wind speed.
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