The mean and variance of the reciprocal merit factor of four classes of binary sequences

TL;DR

This paper derives exact formulas for the mean and variance of the reciprocal merit factor across four classes of binary sequences, deepening understanding of their distribution and asymptotic behavior in digital communication contexts.

Contribution

It provides the first elementary derivation of the mean and variance of the reciprocal merit factor for four key classes of binary sequences, revealing their asymptotic properties.

Findings

The reciprocal merit factor converges in probability to a constant for each class.

Exact formulas for mean and variance are obtained for all four classes.

The results deepen understanding of merit factor distribution in binary sequences.

Abstract

The merit factor of a $\{-1, 1\}$ binary sequence measures the collective smallness of its non-trivial aperiodic autocorrelations. Binary sequences with large merit factor are important in digital communications because they allow the efficient separation of signals from noise. It is a longstanding open question whether the maximum merit factor is asymptotically unbounded and, if so, what is its limiting value. Attempts to answer this question over almost sixty years have identified certain classes of binary sequences as particularly important: skew-symmetric sequences, symmetric sequences, and anti-symmetric sequences. Using only elementary methods, we find an exact formula for the mean and variance of the reciprocal merit factor of sequences in each of these classes, and in the class of all binary sequences. This provides a much deeper understanding of the distribution of the merit factor in these four classes than was previously available. A consequence is that, for each of the four classes, the merit factor of a sequence drawn uniformly at random from the class converges in probability to a constant as the sequence length increases.