## 4 Simulation and Discussion
### Simulation Setup and Baseline Schemes  
The PTs (CUs), STs (DTs), and SRs (DRs) are randomly located in a single-cell cellular network. Unless otherwise specified, all numerical results derive from statistically rigorous Monte Carlo simulations based on 1000 independent trials conducted under varying channel conditions. We conduct the simulation evaluations on a laptop with a Ryzen 9 7945 HX, an NVIDIA GeForce RTX 4080, and 64 GB memory. Some common simulation parameters are listed as follows and can also be found in Table 4.  
The cell radius is \(R=500\) m. Referring to the 3 GPP 38.811 S-band (2-4 GHz) urban scenario, we adopt a log-distance path-loss model for large-scale fading with a path-loss exponent 3.8 and path-loss coefficient \(10^{3.453}\). For small-scale fading gains, we adopt a Rayleigh fading model and model them as independent and identically distributed (i.i.d.) circularly symmetric complex Gaussian (CSCG) random variables, with a mean of zero and a variance of one.  
The number of PLs is fixed as \(M=10\), and the number of SLs is \(N=5\sim 15\). The Gaussian noise spectral density is \(n_{0}=-174\) dBm/Hz. The weights of \(\rho_{m}\) and \(\beta_{n}\) are \(\mu_{m}=1\) and \(\
u_{n}=1\).  
We set the granularities of power and spectrum-sharing factor levels as \(\Delta p=1\) dB and \(\Delta\theta=\)0.01, respectively. The minimum and maximum transmit power are \(p_{min}=-40\) dBm and \(p_{max}=23\) dBm. The required minimum SEs are \(Q_{\mu,m}=10\) bps/Hz and \(Q_{\beta,n}=5\) bps/Hz. Moreover, the step tolerance is set as \(\varepsilon=10^{-5}\) for the stopping criterion.  
The proposed scheme is compared with the following three baseline schemes.  
1. Optimal scheme: This strategy found the optimal solution via the exhaustive search for all possible spectrum allocation and power control. Its link matching is still carried out through the KM algorithm because the KM algorithm is the optimal matching solution in our system;  
2. DRL-based scheme: Referring to our prior work [37], we utilize DRL to handle spectrum allocation and power control problems within each link pair in a distributed manner. Then, this strategy uses the KM algorithm to solve the optimal link-matching problem between multiple link pairs. Unlike the original algorithm in work [37], we modify the objective function to minimize system WSP;  
3. Random scheme: The spectrum allocation, power control, and link matching are randomly performed. It is utilized as a lower-performance benchmark in most related works;  
4. Optimal WSEE maximization scheme: In this approach, we aim to maximize system WSEE. To be more specific, the spectrum allocation and power control are achieved via an  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Value** \\ \hline Cell radius, \(R\) & 500 m \\ Number of PLs, \(M\) & 10 \\ Number of SLs, \(N\) & 5–15 \\ Granularity of power levels, \(\Delta p\) & 1 dB \\ Granularity of spectrum-sharing factor levels, \(\Delta\theta\) & 0.01 \\ Gaussian noise spectral density, \(n_{0}\) & \(-174\) dBm/Hz \\ Weight of \(\rho_{m}\), \(\mu_{m}\) & 1 \\ Weight of \(\beta_{m}\), \(\
u_{n}\) & 1 \\ QoS requirement of \(\rho_{m}\), \(Q_{\beta,m}\) & 10 bps/Hz \\ QoS requirement of \(\beta_{m}\), \(Q_{\beta,n}\) & 5 bps/Hz \\ Minimum transmit power, \(p_{min}\) & \(-40\) dBm \\ Maximum transmit power, \(p_{max}\) & 23 dBm \\ Step tolerance \(\varepsilon\) & \(10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
exhaustive search while the link matching is implemented using the KM algorithm. The WSEE weight for each link is set to be equal.