## 5 Simulation and Analysis
### Comparison of Methods  
In the field of discrete optimisation, representative optimisation algorithms include gradient descent algorithm (GD), dynamic programming algorithm (DP), and genetic algorithm (GA). The set of strategies corresponding to the primary jammers is obtained by the above three algorithms, and the performance of the optimisation algorithm proposed in this paper is verified by comparing the utility function values and BERs with those of the algorithm. The relevant parameters of the comparison algorithm are set as follows: (1) GD: the learning rate set to 0.03, the number of iterations to 350, the momentum term to 0.8, the L2 regularisation factor to \(10^{-3}\), and the decision change stopping threshold to \(10^{-3}\). (2) DP: the resource quantity is set to 110, the resource discretisation slot is 10, and the pruning threshold is 100. (3) GA: the coding method is binary, with a population size of 90, a crossover probability of 0.8, a variance probability of 0.02, and an elite retention of 3. Table 2 represents a comparison of the performance of the methods. From the point of view of time complexity, the proposed algorithm and the GD algorithm have the lowest time complexity, and the DP algorithm has the highest. From the point of view of interference success rate, the proposed algorithm in this paper has the highest success rate and the GD algorithm has the lowest success rate. The GA algorithm solution quality is close to the global optimum, but its dependence on parameter tuning is strong. Meanwhile, GA algorithms are good at global exploration but lack fine-grained local search capability, which greatly increases unnecessary repeated computations. The DP algorithm also achieves a high success rate, but its computation of the exact solution consumes a large amount of time and computational resources, which makes it unable to satisfy the real-time requirements of the jamming scenarios. The GD algorithm, on the other hand, tends to fall into the local optimum, and it is not able to achieve stable and successful jamming, although it achieves an efficient computation in terms of time. In addition, saddle points with gradients close to zero are more common than local minima in high-dimensional spaces, where gradient descent may stall, which can lead to interference failures from time to time. Figure 12: Coverage of three distributed interference topologies. In this study, the BER variation rule with the signal-to-noise ratio (SNR) of the four optimisation methods is demonstrated in Figure 13 under the scenario that the forwarded interference signal intrudes into the receiver. The experimental results show that in the low SNR region (SNR < 8 dB), the system BER is mainly dominated by the environmental noise, and at this time, the performance difference between the anti-jamming algorithms does not show significant differentiation. When the SNR is increased to more than 10 dB, the influence of environmental noise shows an exponential decay trend. At this time, the forwarding interference signal becomes the core factor restricting the BER, and the performance difference of the algorithms starts to appear significantly. The retransmitted interference signal can break through the limitation of the traditional filter and penetrate into the demodulation module at the receiver. It is worth noting that the optimisation algorithm proposed in this study shows a significant advantage in the 12-18 dB high SNR interval, which can control the BER stably at \(6\times 10^{-2}\), while the GD algorithm has a lower BER in this interval, which verifies its lack of interference capability. Figure 14 shows the values of the interference mean utility function under different optimisation methods. It can be seen that the interference mean utility functions of all the methods show a tendency of increasing and then decreasing. This phenomenon can be attributed to the effect of the variation in the number of primary jammers on the interference gains.