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A MINORIZATION-MAXIMIZATION (MM) ALGORITHM FOR ARTIFICIAL NOISE (AN)-BASED MIMOME SECRECY RATE MAXIMIZATION

Citation Author(s):
Mudassir Masood, Ali Ghrayeb, Prabhu Babu, Issa Khalil, Mazen Hasna
Submitted by:
Mudassir Masood
Last updated:
7 December 2016 - 2:36am
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
Mudassir Masood
Paper Code:
1114

Abstract 

Abstract: 

We consider the problem of secrecy rate maximization in a multi-input multi-output multi-eavesdropper (MIMOME) wiretap channel and present an exact solution. A general system model with a multi-antenna eavesdropper and a multi-antenna full-duplex receiver is considered. In particular, we perform joint beamforming and artificial noise optimization in an effort to maximize the achievable secrecy rate. The optimization is performed in the presence of artificial noise generated by both transmitter and legitimate receiver. The resulting optimization problem is non-convex and difficult to solve. We develop a minorization-maximization algorithm to solve the problem. It is iterative in nature and guaranteed to converge to a locally optimal solution. The results can therefore be used to benchmark existing methods. Numerical results are presented to demonstrate the efficacy of the proposed approach.

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A Minorization-Maximization (MM) Algorithm for artificial-noise (AN)-based MIMOME secrecy rate maximization

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