Joint Precoding and Artificial Noise Design for MU-MIMO Wiretap Channels

Eunsung Choi, Mintaek Oh, Jinseok Choi, Jeonghun Park, Namyoon Lee, Naofal Al-Dhahir

Research output: Contribution to journalArticlepeer-review


Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Communications
Publication statusAccepted/In press - 2022

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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