Deep Learning-Based Secure Tag Selection in BackCom Network With RIS-Induced Interference

This article investigates the secrecy performance of a non-linear energy-harvesting backscatter communication (BackCom) network in the presence of direct link and reconfigurable intelligent surface (RIS) interference. The network comprises a source, multiple passive tags, an RIS, and a legitimate reader, with an eavesdropper attempting to intercept the communication. We analyze a tag selection scheme based on long-short-term memory (LSTM) to address the challenge of selecting tags under the influence of direct link and the RIS interference. The nonideal behavior of the RIS is exploited to enhance secrecy performance by modeling RIS phase errors using Von Mises and uniform distributions. Because of interference from the direct link and the RIS being common to all tags, the secrecy rates of different tags are correlated. The LSTM-based scheme effectively captures this correlation and perfectly matches the conventional selection scheme on low and high tag counts. The secrecy outage probability (SOP) achieved using the LSTM outperforms other machine learning techniques, such as $k$ -nearest neighbors ( $k$ -NN), decision trees (DT), and support vector machines (SVM). We also demonstrate the impact of RIS elements, phase error parameters, and the number of tags on the SOP in the considered RIS-aided BackCom network.