Good random matrices over finite fields

The random matrix uniformly distributed over the set of all $m$-by-$n$ matrices over a finite field plays an important role in many branches of information theory. In this paper a generalization of this random matrix, called $k$-good random matrices, is studied. It is shown that a $k$-good random $m$-by-$n$ matrix with a distribution of minimum support size is uniformly distributed over a maximum-rank-distance (MRD) code of minimum rank distance min{$m,n$}$-k+1$, and vice versa. Further examples of $k$-good random matrices are derived from homogeneous weights on matrix modules. Several applications of $k$-good random matrices are given, establishing links with some well-known combinatorial problems. Finally, the related combinatorial concept of a $k$-dense set of $m$-by-$n$ matrices is studied, identifying such sets as blocking sets with respect to $(m-k)$-dimensional flats in a certain $m$-by-$n$ matrix geometry and determining their minimum size in special cases.