Xu, H;
Wong, KK;
Caire, G;
(2023)
SD-Based Low-Complexity Precoder Design for Gaussian MIMO Wiretap Channels.
In:
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings.
(pp. pp. 612-618).
IEEE: Rio de Janeiro, Brazil.
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Abstract
This paper considers a Gaussian multi-input multi-output (MIMO) multiple access wiretap (MAC-WT) channel, where an eavesdropper (Eve) wants to extract the confidential information of all users. Assuming that both the legitimate receiver and Eve jointly decode their interested messages, we aim to maximize the sum secrecy rate of the system by precoder design. Although this problem could be solved by first using the iterative majorization minimization (MM) based algorithm to get a sequence of convex log-determinant optimization subproblems and then using some general tools, e.g., the interior point method, to deal with each subproblem, this strategy involves quite high computational complexity. Therefore, we propose a simultaneous diagonalization based low-complexity (SDLC) method to maximize the secrecy rate of a simple one-user wiretap channel, and then use this method to iteratively optimize the covariance matrix of each user. Simulation results show that in contrast to the existing approaches, the SDLC scheme achieves similar secrecy performance but requires much lower complexity.
| Type: | Proceedings paper |
|---|---|
| Title: | SD-Based Low-Complexity Precoder Design for Gaussian MIMO Wiretap Channels |
| Event: | 2022 IEEE Globecom Workshops (GC Wkshps) |
| Dates: | 4 Dec 2022 - 8 Dec 2022 |
| ISBN-13: | 9781665459754 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GCWkshps56602.2022.10008482 |
| Publisher version: | https://doi.org/10.1109/GCWkshps56602.2022.1000848... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Simulation, Receivers, Minimization, Iterative algorithms, Data mining, Covariance matrices, Computational complexity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164560 |
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