Wiretap Code Design by Neural Network Autoencoders
Wiretap Code Design by Neural Network Autoencoders
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DOI:
10.1109/tifs.2019.2945619
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发表时间:
2020
影响因子:
6.8
通讯作者:
Karl-Ludwig Besser;Pin-Hsun Lin;C. Janda;Eduard Axel Jorswieck
中科院分区:
文献类型:
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作者:
Karl-Ludwig Besser;Pin-Hsun Lin;C. Janda;Eduard Axel Jorswieck
In industrial machine type communications, an increasing number of wireless devices communicate under reliability, latency, and confidentiality constraints, simultaneously. From information theory, it is known that wiretap codes can asymptotically achieve reliability (vanishing block error rate (BLER) at the legitimate receiver Bob) while also achieving secrecy (vanishing information leakage (IL) to an eavesdropper Eve). However, under finite block length, there exists a tradeoff between the BLER at Bob and the IL at Eve. In this work, we propose a flexible wiretap code design for degraded Gaussian wiretap channels under finite block length, which can change the operating point on the Pareto boundary of the tradeoff between BLER and IL given specific code parameters. To attain this goal, we formulate a multi-objective programming problem, which takes the BLER at Bob and the IL at Eve into account. During training, we approximate the BLER by the mean square error and the IL by schemes based on Jensen’s inequality and the Taylor expansion and then solve the optimization problem by neural network autoencoders. Simulation results show that the proposed scheme can find codes outperforming polar wiretap codes (PWC) with respect to both BLER and IL simultaneously. We show that the codes found by the autoencoders could be implemented with real modulation schemes with only small losses in performance.