An Algorithm for Global Maximization of Secrecy Rates in Gaussian MIMO Wiretap Channels
Sergey Loyka, Charalambos D. Charalambous

TL;DR
This paper introduces a globally convergent iterative algorithm using convex optimization techniques to find the optimal transmit covariance matrix for maximizing secrecy rates in Gaussian MIMO wiretap channels, addressing a longstanding open problem.
Contribution
It develops a novel barrier method-based algorithm with guaranteed global convergence for the general case, including non-degraded channels, and provides practical implementation insights.
Findings
Algorithm achieves high-precision solutions with 20-40 Newton steps.
Effective for large systems and can be simplified for degraded channels.
Can incorporate per-antenna power constraints and solve dual problems.
Abstract
Optimal signaling for secrecy rate maximization in Gaussian MIMO wiretap channels is considered. While this channel has attracted a significant attention recently and a number of results have been obtained, including the proof of the optimality of Gaussian signalling, an optimal transmit covariance matrix is known for some special cases only and the general case remains an open problem. An iterative custom-made algorithm to find a globally-optimal transmit covariance matrix in the general case is developed in this paper, with guaranteed convergence to a \textit{global} optimum. While the original optimization problem is not convex and hence difficult to solve, its minimax reformulation can be solved via the convex optimization tools, which is exploited here. The proposed algorithm is based on the barrier method extended to deal with a minimax problem at hand. Its convergence to a global…
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