# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 5 Simulation Results and Discussion  
### Simulation of Time-Frequency Domain Feature Extraction  
The purpose of the time-frequency domain feature extraction of the unknown jamming pattern is to obtain the head node of the unknown jamming pattern in the anti-jamming knowledge graph. We adopt the recall of the time-frequency domain features extracted to verify the performance of the proposed algorithm. Recall is a metric widely used in the field of information retrieval and statistics to evaluate the quality of the search results. The formula of recall can be expressed as:  
\[R=\frac{TP}{TP+FN} \tag{8}\]  
where \(R\) is the recall, \(TP\) is the number of positive samples recognized, and \(FN\) is the number of the unrecognized positive samples. Recall can well demonstrate the ability and effect of positive sample recognition. In our simulations, recall is computed as a measure for the quality of feature extraction, where \(TP\) represents the count of successful time-frequency domain feature extraction, and \(FN\) represents the count of the failed ones. The validation process is: firstly, adopt the algorithm to extract the time-frequency domain features of the input jamming patterns with known parameters; then, the recall of feature extraction in different domains are computed and analyzed. We initialize sensing parameters to simulate the real data from the jamming cognition module for the simulation experiment. The sensing parameters details are shown in Table 3. Here, we only take frequency domain feature extraction as an example considering the paper length limit. Related parameters used in our simulations are set as follows: the available communication frequency band is set to be 30 MHz-2.5 GHz, namely the communication \begin{table}
\begin{tabular}{c c c} \hline \hline
**Questions** & **Tag** & **Queries** \\ \hline Which jamming patterns have frequency gaps? & 1 & MATCH p=(m:’Jamming pattern’)-[*]->(m:’Features of jamming pattern’[name:’Instantaneous frequency gap exists’]) RETURN p \\ How to counter sweep jamming? & 2 & MATCH p=(m:’Jamming pattern’[name:’Swepep jamming’])-[r:’Ant-jamming strategy’]->(p) RETURN p \\ Which frequency bands should be adjusted for adaptive frequency hopping? & 4 & MATCH p=(m:’Anti-jamming strategy’[name:’Adaptive frequency hopping’])-[r:’Target’]->(p) RETURN p \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Questions and corresponding query template examples. bandwidth 5 MHz. The starting and stopping frequency range of the jamming pattern is set to be 30 MHz-2.5 GHz; the bandwidth of partial frequency band jamming and multi-tone jamming range is set to be 500 kHz-300 MHz; the instantaneous bandwidth of high-speed collision jamming and sweep jamming range is set to be 500 kHz\(\sim\)300 MHz. Perception error rate is the ratio of the perceived frequency deviation to the true value from the jamming sensing module. The simulation results are shown in Figure 4.  
The simulation result shows that the recall of frequency domain feature extraction is higher when the jamming pattern is narrowband or instantaneous narrowband. It is also proved that the proposed feature extraction method has strong robustness against perception error. Our analysis suggests that when the bandwidth of the jamming signal is relatively narrow compared with the communication frequency band, the jamming cannot cover the available frequency band, and the jamming parameters perceived under a certain error cannot cover the available frequency band. Thus, the original frequency domain features are still maintained, and the feature extraction algorithm can recognize and extract in this case. Compared with narrowband or instantaneous narrowband jamming patterns, the recall of frequency domain feature extraction of wideband block jamming patterns decreases slowly with growing perception error because the bandwidth of the jamming signal is relatively wider than the communication frequency band, which is consistent with our analysis.