# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 5 Simulation Results and Discussion
### Simulation of Decision-Making Result
#### 5.2.2 Simulation of the Decision-Making Result of Unknown Jamming Patterns  
In this section, we assume that the perception module of the system fails to provide the parameters of the jamming patterns, but still successfully provides the jamming time-frequency domain parameters. After extracting the time-frequency domain features of the unknown jamming patterns, the algorithm searches the corresponding strategies based on the acquired time-frequency domain features, then outputs the decision. The method to verify the accuracy of the decision-making algorithm of unknown jamming patterns is to input the parameters of the generated jamming pattern but set the pattern as unknown (the jamming patterns generated by this method are called pseudo-unknown patterns of the original jamming). Then, the simulations verify whether the decision result of this pseudo-unknown jamming pattern is accurate or not. The simulation results are shown in Figure 7.  
Figure 7 shows results of algorithm performance when different pseudo-unknown jamming patterns are considered, including single-tone jamming, partial frequency band jamming, comb jamming, high-speed collision jamming, wideband block jamming and sweep jamming. It can be seen from the simulation results that for single-tone jamming, partial frequency band jamming, comb jamming, high-speed collision jamming and wideband  
Figure 6: Decision accuracy curves of known jamming patterns under perception errors. block jamming, the decision-making method has good robustness, the decision accuracy is around 90%, and the reliability is less affected by the perception error. Different from the case where the known pattern is sweep jamming, the accuracy of the decision-making algorithm gradually becomes stable when the perceptual error rate increases. For the pseudo-unknown pattern of sweep jamming, the decision-making accuracy is affected by the perceptual error to a certain extent. The analysis believes that this is because the anti-jamming strategy is obtained by searching the anti-jamming knowledge graph in the case of known styles, the possible range of the anti-jamming strategy is smaller, and the curve of the decision accuracy is rather smooth. In the case of an unknown pattern, the time-frequency domain features of the jamming pattern are obtained by the time-frequency domain feature extraction algorithm, and the anti-jamming strategy can be selected in a large range, which leads to a certain decrease in the accuracy due to the influence of the perception error. As can be seen from the above simulation results, the proposed anti-jamming decision-making algorithm has great advantages over the existing methods in terms of the number of jamming patterns and decision-making time. The specific comparison is given in the Table 5.  
Existing methods mainly achieved their optimized decision-making for a particular jamming pattern based on a few communication system parameters, such as communication frequency and transmission power. The resultant optimizing results may not render the best results when more communication parameters need to be considered. By comparison, the proposed decision-making algorithm based on the anti-jamming knowledge \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Deal with the** & **Decision-Making** & **Accuracy** & **Iteration** & **Decision-Making** \\  & **Jamming Pattern** & **Domain** & & **Time** \\ \hline \hline Based on & & & & & \\ Knowledge Graph & Multiple & Multiple & Most above 90\% & 1 & Milliseconds \\ Reinforcement learning & 1 & 1–2 & Not sure & Vast quantities & Seconds \\ (i.e., Q-learning) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with existing work. Figure 7: Decision accuracy curves of unknown jamming patterns under perception errors. graph can not only deal with multiple jamming patterns, but can also consider multiple communication parameters simultaneously.