Deep learning based physical layer security of D2D underlay cellular network
Lixin Li, Youbing Hu, Huisheng Zhang, Wei Liang, Ang Gao
Abstract
In order to improve the physical layer security of the device-to-device (D2D) cellular network, we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning. Due to the mobility of users, using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication. Therefore, in this paper, we utilize the Echo State Network (ESN) to select the transmit antenna and the Long Short-Term Memory (LSTM) to establish the D2D pair. The simulation results show that the LSTM-based and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.