# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 1 Introduction  
In recent years, a huge amount of private information has been transmitted and shared through many kinds of communication and broadcasting platforms, such as the internet of things (IoT), the digital broadcast network, and the wireless sensor network (WSN) [1; 2; 3; 4]. Because important and private information such as clinical records, bank accounts, and credit card information can be transmitted and shared over the communication network, communication security is required. Traditionally, cryptographic protocols have been researched and adopted to secure digital communication systems [5; 6; 7; 8; 9]. Physical layer security has recently become an emerging hot topic in digital communication and broadcasting systems [10], and various physical layer security technologies have been investigated [11; 12; 13; 14; 15; 16; 17; 18; 19]. [PERSON] briefly introduced the issues and opportunities for applying physical layer security to real systems [11], and [PERSON] et al. provided an overview of research results in information-theoretic security [12]. However, most existing studies have used an artificial noise generation scheme to obtain physical layer security function [13; 14; 15; 16]. Although the authors of [17; 18] described some analysis and service models for physical layer security, these did not depict clear analytic and mathematical results. Furthermore, recently, various physical layer security techniques have been investigated. The authors of [20] proposed a mixed integer linear program (MILP)-based security technique for physical layer security in elastic optical networks. The proposed technique can provide high-level encryption operation results. However, its limitation is that it is impractical for large-scale networks, due to its very high computational complexity and the fact that it requires a lot of resources. The authors of [21] proposed a method to enhance physical layer security using an intelligent reflecting surface (IRS). This technique improves security performance by using a convolutional neural network (CNN)-based model, even when the instantaneous channel information of the eavesdropper is unknown. However, applying a CNN-based algorithm requires a large amount of high-quality data, and since IRS technology is still in its early stages, this could pose a challenge to its practical implementation. The authors of [22] proposed a method based on RF fingerprint technology to enhance the security of IoT devices. The proposed technology can identify internet of things (IoT) devices with a high accuracy through RF fingerprints by utilizing a machine learning-based model. However, RF fingerprints are greatly affected by environmental conditions such as channel conditions and signal-to-noise ratio (SNR), making it difficult to operate stably. In addition, frequent environmental changes based on such uncertainties lead to the limitation that machine learning training must be performed frequently. Therefore, there are limitations in practicality. This paper presents a novel physical layer security technique based on non-orthogonal signal processing. For the non-orthogonal signaling, Faster-than-Nyquist (FTN) signaling is considered [23; 24]. FTN signaling is a communication technique that enhances spectral efficiency [25; 26; 27; 28]. FTN signaling can simply accomplish high spectral efficiency based on a faster symbol rate than the Nyquist rate. However, because the use of a faster symbol rate than the Nyquist rate destroys the orthogonality between symbols, inter-symbol interference (ISI) is inevitably generated. To reduce the ISI and detect the transmitted symbols, the receiver should know and utilize the information about symbol rate or ISI values. In the turbo equalizer based on the Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm, the ISI filter values are used for BCJR tap coefficients in FTN signaling [29; 30]. In this paper, a physical layer security technique based on FTN signaling is proposed.