The square @math of a linear error correcting code @math is the linear code spanned by the component-wise products of every pair of (non-necessarily distinct) words in @math . Squares of codes have gained attention for several applications mainly in the area of cryptography, and typically in those applications, one is concerned about some of the parameters (dimension and minimum distance) of both @math and @math . In this paper, motivated mostly by the study of this problem in the case of linear codes defined over the binary field, squares of cyclic codes are considered. General results on the minimum distance of the squares of cyclic codes are obtained, and constructions of cyclic codes @math with a relatively large dimension of @math and minimum distance of the square @math are discussed. In some cases, the constructions lead to codes @math such that both @math and @math simultaneously have the largest possible minimum distances for their length and dimensions.
If C is a binary linear code, let C〈2〉 be the linear code spanned by intersections of pairs of codewords of C. We construct an asymptotically good family of binary linear codes such that, for C ranging in this family, C〈2〉 also form an asymptotically good family. For this, we use algebraic-geometry codes, concatenation, and a fair amount of bilinear algebra. More precisely, the two main ingredients used in our construction are, first, a description of the symmetric square of an odd degree extension field in terms only of field operations of small degree, and second, a recent result of Garcia-Stichtenoth-Bassa-Beelen on the number of points of curves on such an odd degree extension field. Given a linear code @math , one can define the @math -th power of @math as the span of all componentwise products of @math elements of @math . A power of @math may quickly fill the whole space. Our purpose is to answer the following question: does the square of a code "typically" fill the whole space? We give a positive answer, for codes of dimension @math and length roughly @math or smaller. Moreover, the convergence speed is exponential if the difference @math is at least linear in @math . The proof uses random coding and combinatorial arguments, together with algebraic tools involving the precise computation of the number of quadratic forms of a given rank, and the number of their zeros.