Rank minimization over finite fields

This paper establishes information-theoretic limits in estimating a finite field low-rank matrix given random linear measurements of it. Necessary and sufficient conditions on the number of measurements required are provided. It is shown that these conditions are sharp. The reliability function associated to the minimum-rank decoder is also derived. Our bounds hold even in the case where the sensing matrices are sparse. Connections to rank-metric codes are discussed.