## 3 Verification and Analysis
### Typical Scenario Analysis  
In a 50 km \(\times\) 50 km area, there are two groups of communication devices. Each group consists of four defensive intelligent agents. The communication devices within each group communicate randomly with each other. Within a 40-km radius of the communication devices, there are eight attacking intelligent agents. The attacking devices interfere with all defensive devices within the interference frequency band. When _SINR_ exceeds 10 dB, interference is considered present, and the interference level is calculated according to the corresponding formula. The red team defensive agents and the blue team jamming intelligent agents move in real-time, and the relative positional relationship between the communication devices and the jamming devices is illustrated in Figure 3.  
The parameter settings for the red team defensive and blue team attacking agents are shown in Table 6. In this simulation, the red team defensive agents have five selectable communication frequencies within the 30 MHz to 3000 MHz range: 40 MHz, 500 MHz, 900 MHz, 2200 MHz, and 2700 MHz. The transmission power (Pt), transmission antenna gain, and reception antenna gain (Gr) are set as specified in Table 6. The location information for the communication devices is updated in real-time. This simulation is configured with 8 reference locations, as shown in Table 6. The modulation scheme is set to 6, and the coding scheme is set to 'MPSK'. The blue team jamming agents have 3 selectable interference frequency bands within the 30 MHz to 3000 MHz range: 30 MHz to 300 MHz, 300 MHz to 1000 MHz, and 1000 MHz to 3000 MHz. The transmission power (Pt), transmission antenna gain, and reception antenna gain (Gr) are specified in Table 6. The location information for the jamming devices is also updated in real-time, with 8 reference locations set for this simulation, as indicated in Table 6. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Interference** & \multicolumn{8}{c}{**Interference Level**} \\
**Parameters** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** & **Level 6** & **Level 7** & **Level 8** & **Level 9** & **Level 10** \\ \hline _SINR_ & 10 dB & 10 dB & 10 dB & 10 dB & 10 dB & 10 dB & 10 dB & 10 dB & 10 dB & 10 dB \\ \(R_{bw}\) & 10\% & 20\% & 30\% & 40\% & 50\% & 60\% & 70\% & 80\% & 90\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification standards for blocking jamming levels are as follows. The software in this paper is primarily developed using Qt Creator 5.12.12, while the MCCFR algorithm is implemented on the Python platform and generates a \".pyd\" file, which is then called by Qt. Creator. This simulation wargaming software uses the frequency usage parameters of both the red and blue sides, as shown in Table 6, as input. During the simulation process, the red-side agents intelligently select frequencies for communication using the MCCFR algorithm based on predefined trajectory and frequency usage information to avoid interference. Meanwhile, the blue side employs the MCCFR algorithm to select frequencies for interference intelligently. Figure 4 illustrates the simulation scenario constructed using the developed software. During the simulation, the red and blue opposing teams move along predefined trajectories and make frequency allocation decisions based on the MCCFR algorithm. In the figure, the black sectors represent the blue team's jamming areas and directions, the red lines indicate disrupted communications between the red and blue teams. Figure 3: Position distribution diagram of the red and blue teams. red team agents due to jamming, and the green lines signify successful link establishment and normal communication for the red team. In the simulation interface, partial information is displayed below the red and blue agents, including the blue team's jamming power and the red team's signal-to-noise ratio (SNR), jamming-to-signal ratio (JSR), and bit error rate (BER).