Optimal power allocation for secure estimation of multiple parameters

Abstract

Optimal power allocation for secure estimation of multiple deterministic parameters is investigated under a total power constraint. The goal is to minimize the Cramér-Rao lower bound (CRLB) at an intended receiver while keeping estimation errors at an eavesdropper above specified target levels. To that end, an optimization problem is formulated by considering measurement models involving linear transformation of the parameter vector and additive Gaussian noise. Although the proposed optimization problem is nonconvex, it is decomposed into convex sub-problems by utilizing the structure of the secrecy constraints. Then, optimal solutions to the sub-problems are characterized via optimization theoretic approaches. An algorithm utilizing that characterization is developed to obtain the optimal solution of the proposed problem.