Joint Precoder and Artificial Noise Design for MIMO Wiretap Channels With Finite-Alphabet Inputs Based on the Cut-Off Rate

• Published in: IEEE transactions on wireless communications Vol. 16; no. 6; pp. 3913 - 3923
• Main Authors: Aghdam, Sina Rezaei, Duman, Tolga M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antennas; Approximation; artificial noise; Channels; Coefficients; Complexity; Covariance matrices; Cut-off; cut-off rate; Ergodic processes; Exact solutions; finite-alphabet inputs; Maximization; MIMO; MIMO communications; Mutual information; Noise prediction; Optimization; Physical layer security; Precoding; Receivers; Searching; Simplification; Statistics; Transmitters
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2017.2690279

We consider precoder and artificial noise (AN) design for multi-antenna wiretap channels under the finite-alphabet input assumption. We assume that the transmitter has access to the channel coefficients of the legitimate receiver and knows the statistics of the eavesdropper's channel. Accordingly, we propose a secrecy rate maximization algorithm using a gradient descent-based optimization of the precoder matrix and an exhaustive search over the power levels allocated to the AN. We also propose algorithms to reduce the complexities of direct ergodic secrecy rate maximization by: 1) maximizing a cut-off rate-based approximation for the ergodic secrecy rate, simplifying the mutual information expression, which lacks a closed-form and 2) diagonalizing the channels toward the legitimate receiver and the eavesdropper, which allows for employing a per-group precoding-based technique. Our numerical results reveal that jointly optimizing the precoder and the AN outperforms the existing solutions in the literature, which rely on the precoder optimization only. We also demonstrate that the proposed low complexity alternatives result in a small loss in performance while offering a significant reduction in computational complexity.