# Machine Learning-Assisted Secure Random Communication System
## 4 Transmission
#### Transmitter 'T'  
The transmitter 'T,' shown in Figure 4, operates on the principle of stochastic process shift keying or noise shift keying (NSK). It transmits the incoming binary information, namely '0' and '1', by generating R0\(\sim\)S\({}_{\alpha_{0}}\) (\(\beta_{0}\), \(\gamma\), \(\mu\)) and R1\(\sim\)S\({}_{\alpha_{1}}\) (\(\beta_{1}\), \(\gamma\), \(\mu\)) noises as random carrier signals. Because \(\beta_{1}=~{}-\beta_{0}\), the symmetry behind the skewness parameters '\(\beta_{0}\)' and '\(\beta_{1}\)' are exploited to shift the SkaS noise distributions to either the left or right side, respectively. This is intentionally performed to generate noise samples from distributions \(S_{\alpha_{0}}\) (\(\beta_{0}\), \(\gamma\), \(\mu\)) and \(S_{\alpha_{1}}\) (\(\beta_{1}\), \(\gamma\), \(\mu\)) to represent the corresponding '0' and '1' values. The term \(\Delta\beta\triangleq~{}\beta_{1}-~{}(\beta_{0})\) is used to monitor the difference between \(\beta_{0}\) and \(\beta_{1}\). The samples are generated as per the predetermined amount, which is known as the pulse length '\(T_{s}N\)' or simply \(N\), as \(T_{s}=1\) is considered throughout this study. It is necessary to encode a unit bit as '1' or '0' in the following form:  
\[\textbf{R}~{}=~{}\{r_{1},\,r_{2},\,\ldots,\,r_{N}\} \tag{4}\]  
where \(N\) is the total generated noise sample of length \(T_{s}\), which corresponds to a single binary bit. The binary information '0' and '1', hidden in random carriers R0\(\sim\)S\({}_{\alpha_{0}}\) (\(\beta_{0}\), \(\gamma\), \(\mu\)) and R1\(\sim\)S\({}_{\alpha_{1}}\) (\(\beta_{1}\), \(\gamma\), \(\mu\)), respectively, cannot be retrieved without an exact knowledge of \(T_{s}N\)[46; 50]. This method is known as skewed \(\alpha\)-stable noise shift keying (SkaSNSK). The complexity of R0\(\sim\)S\({}_{\alpha_{0}}\) (\(\beta_{0}\), \(\gamma\), \(\mu\)) and R1\(\sim\)S\({}_{\alpha_{1}}\) (\(\beta_{1}\), \(\gamma\), \(\mu\)) can be further increased by selecting different characteristic exponents \(\alpha_{0}\) and \(\alpha_{1}\). However, they are kept the same, as \(\alpha_{0}~{}=~{}\alpha_{1}~{}=~{}\alpha\), throughout the study to analyze the effect of only hiding the binary information on the skewness parameters. Similarly, the scale and location parameters are predetermined between the T and LR, as \(\gamma=1\) and \(\mu=0\), respectively. Consequently, the factors responsible for generating unique random carriers in different settings are \(\alpha\), \(\Delta\beta\), and \(N\).  
Any specific values of \(\alpha\), \(\Delta\beta\), and \(N\), within their defined ranges, must be selected in advance by the T and LR to securely transmit the hidden binary information via SkaSNSK. These values act as secure keys in the data retrieval process via the LR. In this study, they are set to the values listed in Table 2.  
Figure 4: Model of the proposed ML-RCS for two scenarios: (1) transmitter and legitimate receiver in AWGN channel, and (2) transmitter and illegitimate receiver in AWGN channel.  
The values of \(\alpha\) and \(\Delta\beta\), as listed in Table 2, are typically used while experimenting with \(\alpha\)-stable noise signals. However, the values of \(N\) were derived after rigorous experimentation with the values used in [43, 44, 45, 46, 47, 48, 49, 50]. During the experiments, we examined various combinations of the above parameter values to analyze the effects on the performance of ML-RCSs for the T and LR; we also aimed to determine a trend in the performance while exhaustively searching the parameters responsible for SkaSNSK.