Moments of Autocorrelation Demerit Factors of Binary Sequences
Daniel J. Katz, Miriam E. Ramirez

TL;DR
This paper develops new combinatorial methods to analyze the distribution of autocorrelation demerit factors in binary sequences, providing exact formulas for moments like variance, skewness, and kurtosis, which are crucial for sequence design in communications.
Contribution
It introduces novel combinatorial techniques to compute all central moments of the demerit factor for binary sequences, extending previous work on mean and variance.
Findings
All central moments are positive for sequence length ≥ 4.
Exact formulas for variance, skewness, and kurtosis are derived.
The methods confirm and extend previous statistical results on demerit factors.
Abstract
Sequences with low aperiodic autocorrelation are used in communications and remote sensing for synchronization and ranging. The autocorrelation demerit factor of a sequence is the sum of the squared magnitudes of its autocorrelation values at every nonzero shift when we normalize the sequence to have unit Euclidean length. The merit factor, introduced by Golay, is the reciprocal of the demerit factor. We consider the uniform probability measure on the binary sequences of length and investigate the distribution of the demerit factors of these sequences. Sarwate and Jedwab have respectively calculated the mean and variance of this distribution. We develop new combinatorial techniques to calculate the th central moment of the demerit factor for binary sequences of length . These techniques prove that for and , all the central moments are…
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Taxonomy
TopicsMathematical Approximation and Integration
