A Data-Driven Analysis of Secret Key Rate for Physical Layer Secret Key Generation From Wireless Channels

This letter considers a data-driven approach to a secret key rate analysis of physical layer secret key generation based on mutual information neural estimator (MINE), which achieves the state-of-the-art estimation accuracy. Since MINE does not require any statistical knowledge of randomness sources...

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Bibliographic Details
Published in:IEEE communications letters 2023-12, Vol.27 (12), p.3166-3170
Main Authors: Matsumine, Toshiki, Ochiai, Hideki, Shikata, Junji
Format: Article
Language:English
Subjects:
Back propagation
Channel estimation
Estimation
Mutual information
neural network
OFDM
orthogonal frequency-division multiplexing (OFDM)
Physical layer secret key generation
Quantization (signal)
Randomness
Symbols
Training
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Summary:This letter considers a data-driven approach to a secret key rate analysis of physical layer secret key generation based on mutual information neural estimator (MINE), which achieves the state-of-the-art estimation accuracy. Since MINE does not require any statistical knowledge of randomness sources, the proposed approach is applicable to any source model and even real data which cannot be fully expressed by a simple model. We first demonstrate by simulations that the proposed estimation provides good approximations to the theoretical secret key rate for a simple model, and then show that the achievable secret key rate highly depends on sources of randomness and employed channel estimation schemes for a realistic wireless channel model. As an application of our analysis, we present a method to train quantization through backpropagation that maximizes the estimated secret key rate.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3323957