Abstract
The physical layer security is a new security paradigm based on the principles of information theory. Several methods for achieving secrecy in physical layer is proposed. This paper proposes PLS based on the deep learning architecture. In which the deep learning model will transform the channel coefficients, the beamforming based on this transformed channel coefficients can be decoded using deep learning architecture in the receiver. The secrecy rate and secrecy outage probability of proposed system is compared with the zero forcing based beamforming and superior performance is verified by the simulation using popular deep learning library TensorFlow.
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Siyad, C.I., Tamilselvan, S. (2020). Deep Learning Enabled Physical Layer Security to Combat Eavesdropping in Massive MIMO Networks. In: Smys, S., Senjyu, T., Lafata, P. (eds) Second International Conference on Computer Networks and Communication Technologies. ICCNCT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-030-37051-0_72
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DOI: https://doi.org/10.1007/978-3-030-37051-0_72
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