Deep neural network based precoding for wiretap channels with finite alphabet inputs

Date
2021-04-28
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Source Title
IEEE Communications Letters
Print ISSN
2162-2337
Electronic ISSN
2162-2345
Publisher
IEEE
Volume
10
Issue
8
Pages
1652 - 1656
Language
English
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Abstract

We consider secure transmission over multi-input multi-output multi-antenna eavesdropper (MIMOME) wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits the function approximation abilities of deep neural networks (DNNs) for solving the required power allocation problem. It is demonstrated that the DNN learns the optimal power allocation without any performance degradation compared to the conventional technique with a significant reduction in complexity.

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