A technique to study the correlation measures of binary sequences

Based on the cyclotomy classes of extension fields, a family of binary cyclotomic sequences are constructed and their pseudorandom measures (i.e., the well-distribution measure and the correlation measure of order k) are estimated using certain exponential sums. A lower bound on the linear complexity profile is also presented in terms of the correlation measure.

We study measures used to assess Boolean functions, where the functions are understood to be derived from binary sequences. We prove relations between two complexity measures for these Boolean functions and the correlation measure of order k of the corresponding sequence. More precisely, we study sparsity and combinatorial complexity of the Boolean function. Moreover, for a sequence with 'typical' values of the correlation measure of order k we apply these relations to derive bounds on the complexity measures for the corresponding Boolean function. Finally, we apply our results to Sidelnikov sequences for which nice upper bounds on the correlation measure are known.

We study the distribution of s-dimensional points of digital explicit inversive pseudorandom numbers with arbitrary lags. We prove a discrepancy bound and derive results on the pseudorandomness of the binary threshold sequence derived from digital explicit inversive pseudorandom numbers in terms of bounds on the correlation measure of order k and the linear complexity profile. The proofs are based on bounds on exponential sums and earlier relations of Mauduit, Niederreiter and Sárközy between discrepancy and correlation measure of order k and of Brandstätter and the third author between correlation measure of order k and linear complexity profile, respectively.

C. Mauduit and A. Sarkozy proposed the use of well-distribution measure and correlation measure as measures of pseudorandomness of finite binary sequences. In this paper we will introduce and study a further measure of pseudorandomness: the symmetry measure. First we will give upper and lower bounds for the symmetry measure. We will also show that there exists a sequence for which each of the well-distribution, correlation and symmetry measures are small. Finally we will compare these measures of pseudorandomness.

On the correlation of binary sequences

On an inequality between pseudorandom measures of lattices

Recently, Dartyge and Sarkozy defined the measures, i.e., the well- distribution measure and the correlation measure of order k, of pseudo-randomness of subsets of the set {1, 2, . . . , N}, and they presented several examples for subsets with strong pseudo-random properties when N is a prime number. In this article, we present a construction of pseudo-random subsets for N = pq and give some partial results on the pseudo-random measures.

Linear complexity profile and correlation measure of order k are important pseudorandomness measures for sequences used in cryptography. We study both measures for a class of binary sequences called Legendre-Sidelnikov sequences. The proofs involve character sums.

We give finiteness results concerning terms of linear recurrence sequences having a representation as a linear combination, with fixed coefficients, of powers of fixed primes. On one hand, under certain conditions, we give effective bounds for the terms of binary recurrence sequences with such a representation. On the other hand, in the case of some special binary recurrence sequences, all terms having a representation as sums of powers of [Formula: see text] and [Formula: see text] are explicitly determined.

Mauduit and Sárközy introduced and studied certain numerical parameters associated to finite binary sequences $E_N\in\{-1,1\}^N$ in order to measure their 'level of randomness'. Two of these parameters are the normality measure$\cal{N}(E_N)$ and the correlation measure$C_k(E_N)$of order k, which focus on different combinatorial aspects of $E_N$. In their work, amongst others, Mauduit and Sárközy investigated the minimal possible value of these parameters.In this paper, we continue the work in this direction and prove a lower bound for the correlation measure $C_k(E_N)$ (k even) for arbitrary sequences $E_N$, establishing one of their conjectures. We also give an algebraic construction for a sequence $E_N$ with small normality measure $\cal{N}(E_N)$.

In earlier papers finite pseudorandom binary sequences were studied, quantitative measures of pseudorandomness of them were introduced and studied, and large families of “good” pseudorandom sequences were constructed. In certain applications (cryptography) it is not enough to know that a family of “good” pseudorandom binary sequences is large, it is a more important property if it has a “rich”, “complex” structure. Correspondingly, the notion of “f-complexity” of a family of binary sequences is introduced. It is shown that the family of “good” pseudorandom binary sequences constructed earlier is also of high f-complexity. Finally, the cardinality of the smallest family achieving a prescibed f-complexity and multiplicity is estimated.

Autocorrelation Function of a Family of Complex Sequences

In a previous paper, No, Golomb, Gong, Lee and Gaal (see ibid., vol.44, p.814-17, 1998) conjectured that certain binary sequences having a simple trace description possess the ideal autocorrelation property. In the present paper it is shown that each such sequence is balanced and, moreover, that the dual of the linear cyclic code generated by the sequence and its cyclic shifts, is a triple-error correcting code having the same weight distribution as the triple-error correcting Bose-Chaudhuri-Hocquenghem (BCH) code. This cyclic code also contains a cyclic subcode that yields a new family of sequences having the same size and correlation parameters as does the family of Gold sequences.

A classical problem of digital sequence design, first studied in the 1950s but still not well understood, is to determine those binary sequences whose aperiodic autocorrelations are collectively small according to some suitable measure. The merit factor is an important such measure, and the problem of determining the best value of the merit factor of long binary sequences has resisted decades of attack by mathematicians and communications engineers. In equivalent guise, the determination of the best asymptotic merit factor is an unsolved problem in complex analysis proposed by Littlewood in the 1960s that until recently was studied along largely independent lines. The same problem is also studied in theoretical physics and theoretical chemistry as a notoriously difficult combinatorial optimisation problem. The best known value for the asymptotic merit factor has remained unchanged since 1988. However recent experimental and theoretical results strongly suggest a possible improvement. This survey describes the development of our understanding of the merit factor problem by bringing together results from several disciplines, and places the recent results within their historical and scientific framework.

Two approaches to bounding the partial auto- and crosscorrelations of binary sequences are considered. The first approach uses the discrete Fourier transform and bounds for character sums to obtain bounds on partial autocorrelations of m-sequences and on the partial auto- and crosscorrelations for the small Kasami sets and dual-BCH families of sequences. The second approach applies to binary sequences obtained by interleaving m-sequences. A bound on the peak partial correlation of such sequences is derived in terms of the peak partial autocorrelation of the underlying m-sequences. The bound is applied to GMW, No (1987), and other families of sequences for particular parameters. A comparison of the two approaches shows that the elementary method gives generally weaker results but is more widely applicable. On the other hand, both methods show that well-known sequence families can have favorable partial correlation characteristics, making them useful in certain spread-spectrum applications.