## 6 Simulation Analysis  
In this section, MATLAB simulations are conducted to evaluate the performance of the proposed power control algorithm within the subsystems, the impact of weights on subsystem performance, and the performance of the multi-parameter control anti-jamming algorithm for wireless communication systems based on LQR. Simulation 1 compares the performance of the proposed power control algorithm within the wireless communication subsystem with that of the traditional power adaptive algorithm and the power control algorithm base on PID under sudden jamming conditions. Using Binary Phase Shift Keying (BPSK) modulation and (2016, 504) Low-Density Parity-Check Code (LDPC) as examples, the BER curve, after fitting, results in a straight line \(y(t)=-2.4x(t)-10.8\), and then the sensing coefficient \(C=-2.4\). The state weight Q is set to 10, the input weight R is set to 0.001, the simulation duration is 10s, the sampling interval is 0.01 s, and the target BER is \(10^{-4}\). Figure 7 illustrates the simulation comparison of BER curves for the proposed power control algorithm in the subsystem, traditional power adaptive algorithms, and power control algorithms based on PID under burst jamming conditions. Figure 7: Comparison of three power control algorithms under burst jamming. From Figure 6(a), it can be observed that the communication system's transmission channel is AWGN with a signal-to-noise ratio of 30 dB. The system experiences a burst jamming with a power of 5 dBm at t = 5 s, lasting for 0.2 s. Figure 6(b) shows that the proposed algorithm can rapidly respond to jamming, with a response speed significantly superior to existing methods. The proposed algorithm converges to the target BER within 0.01s, even while the jamming persists. Compared to the power control algorithm based on PID and traditional power adaptive methods, the proposed algorithm achieves faster convergence of the output BER to the target BER and exhibits less BER fluctuation. Simulation 2 compares the impact of different weighting factors on the performance of the power control algorithm in the proposed subsystem. From Figure 7(a), it can be observed that the system is subjected to periodic pulse jamming with a power of 5 dBm, lasting 0.2 s with a period of 1 s. Figure 7(b) shows the bit error rate curve with the selected state variable weight Q = 10 and varying control input weights. It is evident that as the control input weight decreases, the oscillation amplitude of the system output becomes more subdued and the response time improves. This is because reducing the control input weight lowers the penalty on the control input, allowing the system to apply a larger control input to more rapidly adjust the state variables to the target values. Figure 8: Comparison of output variations with weight changes for power control algorithms under periodic pulse jamming. In Simulation 3, the performance of the proposed multi-parameter control anti-jamming algorithm for wireless communication systems based on LQR is compared and analyzed with that of the power control algorithm based on LQR for unmodeled switching systems, as well as the multi-parameter control anti-jamming algorithm based on PID, under the condition of random pulse jamming. Assume that the system initially uses Quadrature Phase Shift Keying (QPSK) modulation and (2016, 504) LDPC coding for information transmission. The system is capable of switching between different modulation and coding schemes freely when exposed to external jamming, while maintaining a high transmission rate in the absence of jamming or when jamming is minimal. Using QPSK modulation and BPSK modulation, with both using (2016, 504) LDPC coding, as examples of switching between two subsystems, the BER curves, after fitting, are represented by straight lines \(y_{\text{BER}}(t)=-3.3x_{i}(t)-9\) and \(y_{\text{BER}}(t)=-2.4x_{i}(t)-14.8\), respectively. Therefore, based on the system modeling conclusions, the linear state equations for these two subsystems can be obtained as follows:  
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