## 1 Introduction
### Potential Function Model  
The traditional monostable potential function model is the most studied monostable potential function model in stochastic resonance, with the advantages of fewer parameters and the easy adjustment of the potential function structure. Its potential function expression is as follows:  
\[U_{s}(x)=\frac{1}{2}ax^{2} \tag{1}\]  
where \(a\) represents the width of the monostable potential field. The Gaussian potential function model is a relatively complex monostable potential function model [13]. By adjusting the system parameters, the depth and width of the potential well can be flexibly controlled. Its expression is as follows:  
\[U_{g}(x)=-v\exp(-\frac{x^{2}}{r^{2}}) \tag{2}\]  
where \(v\) represents the intensity of the Gaussian potential field, and \(r\) represents the depth of the Gaussian potential field. Combining the advantages of the traditional monostable potential function and Gaussian potential function, this paper introduces the Gaussian potential function on the basis of the traditional monostable potential function and combines the traditional monostable potential function with Gaussian potential function to obtain an unsaturated bistable potential function, which is expressed as follows:  
\[U(x)=U_{s}(x)-U_{g}(x)=\frac{1}{2}ax^{2}-(-v\exp(-\frac{x^{2}}{r^{2}})) \tag{3}\]  
Take the first derivative of the unsaturated bistable potential function, let \(U^{\prime}(x)=0\), and when \(ar^{2}<2v\), the unsaturated potential function has 2 stable points and 1 unstable point, which are, respectively:  
\[\left\{ \begin{array}{l}x_{s1}=\sqrt{r^{2}\ln(\frac{2v}{ar^{2}})}\\ x_{un}=0\\ x_{s2}=-\sqrt{r^{2}\ln(\frac{2v}{ar^{2}})}\end{array} \right. \tag{4}\]  
OFDM Baseband Complex Signal Model  
The OFDM baseband signal received by the receiver of the SUUBSR system is as follows:  
\[ \begin{array}{l}s(t)=\text{Re}\bigg{[}\sum\limits_{m=0}^{M-1}\sum\limits_{k= 0}^{K-1}d_{m,k}g(t-mT_{s})e^{i(2\pi f_{k}t+\theta_{k})}\bigg{]}\\ =\sum\limits_{m=0}^{M-1}\sum\limits_{k=0}^{K-1}[a_{m,k}\cos(2\pi f_{k}t+\theta_ {k})-\beta_{m,k}\sin(2\pi f_{k}t+\theta_{k})]g(t-mT_{s})\end{array}  \tag{5}\]  
where \(M\) represents the number of subcarriers in the OFDM signal; \(d_{m,k}\) stands for the complex value of the symbol mapped onto the kth subcarrier of the mth OFDM symbol; \(T_{s}\) is the duration of the OFDM signal for one symbol period; and \(f_{k}\) is the frequency of the kth subcarrier. OFDM signal is a classic multicarrier signal, and each subcarrier is mutually orthogonal. It is relatively difficult to directly perform stochastic resonance processing on OFDM signals using the SUUBSR system. Therefore, the OFDM signal input to the system is first segmented according to the symbol period, and then the signal of one symbol period is decomposed into in-phase and quadrature components through orthogonal transformation, which are sent to two stochastic resonance systems, respectively, for the simultaneousstochastic resonance enhancement processing of these two components. The in-phase and quadrature components within the mth OFDM symbol period can be expressed as follows:  
\[\left\{ \begin{array}{l}s_{\rm I}(m,n)=\sum\limits_{k=0}^{K-1}\alpha_{k}\cos( \omega_{k}n+\theta_{k})\\ s_{\rm Q}(m,n)=\sum\limits_{k=0}^{K-1}\beta_{k}\sin(\omega_{k}n+\theta_{k})\\ \end{array} \right. \tag{6}\]  
where \(\omega_{k}=2\pi f/\Delta f\), \(\Delta f\) as the subcarrier spacing, \(\Delta f=K/T_{s}\). In order to better enhance the OFDM baseband complex signal received by the system through stochastic resonance, this paper constructs the SUUBSR system model, which is divided into I-path and Q-path. The real part and imaginary part of the weak OFDM signal are simultaneously processed by stochastic resonance. The system model is as follows:  
\[\left\{