Joint Design of Adaptive Modulation and Precoding for Physical Layer Security in Visible Light Communications Using Reinforcement Learning

There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive M-ary pulse amplitude modulation (PAM...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.82318-82332
Main Authors: Hoang, Duc M. T., Pham, Thanh V., Pham, Anh T., Nguyen, Chuyen T.
Format: Article
Language:English
Subjects:
adaptive modulation
Bit error rate
Channels
Design optimization
Eavesdropping
Error correction
Modulation
Optical design
Optical transmitters
Optimization techniques
Physical layer security
Precoding
Pulse amplitude modulation
Reinforcement learning
Reliability
Security
Signal quality
Visible light communication
Visible light communications
Wireless networks
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Summary:There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive M-ary pulse amplitude modulation (PAM) and precoding, which aims to optimize wiretap visible-light channels' secrecy capacity and bit error rate (BER) performances. The proposed design is motivated by higher-order modulation, which results in better secrecy capacity at the expense of a higher BER. On the other hand, a proper precoding design, which can manipulate the received signal quality at the legitimate user and the eavesdropper, can also enhance secrecy performance and influence the BER. A reward function that considers the secrecy capacity and the BERs of the legitimate user's (Bob) and the eavesdropper's (Eve) channels is introduced and maximized. Due to the non-linearity and complexity of the reward function, it is challenging to solve the optical design using classical optimization techniques. Therefore, reinforcement learning-based designs using Q-learning and Deep Q-learning are proposed to maximize the reward function. Simulation results verify that compared with the baseline designs, the proposed joint designs achieve better reward values while maintaining the BER of Bob's channel (Eve's channel) well below (above) the pre-FEC (forward error correction) BER threshold.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3412055