## 4 Simulation Results
### Numerical Simulation and Discussion
The effects of perfect and imperfect channel estimation are also considered. The SNR of \(\mathbf{H}\) increases from 2 to 30, while the SNR of G is 5 dB. ISQP allocates all the transmission power. In this figure, the solid line shows the lower bound on optimized secrecy capacity. The dashed line shows the lower bound on secrecy capacity without power allocation. The results in Figure 5 are computed according to (6) and (12), and the ISQP algorithm. The results show that the lower bound on secrecy capacity increases with power allocation, implying the high effectiveness of the ISQP algorithm. The secrecy capacity increases with SNR for \(\mathbf{H}\); that is, a low noise level improves secrecy capacity. The lower bound on the secrecy capacity of SCO\(-\)AN decreases when the effect of imperfect channel estimation is taken into consideration. The lower bound on the secrecy capacity with MMSE  
Figure 4: Comparison of Optimization Performance under Perfect and Imperfect Channel Estimation versus Different SNR. channel estimation is greater than the lower bound on that with LS channel estimation. We then reach that the higher channel estimation accuracy enhances secrecy capacity. Figure 6 shows SCO\(-\)AN's secrecy capacity with and without active eavesdropper versus different SNR. The effects of different kinds of channel estimation are also considered. The SNR of \(\mathbf{H}\) increases from 2 to 30, while the SNR of G is 5 dB. ISQP allocates all the transmission power. In this figure, the solid line shows the lower bound on optimized secrecy capacity. The results in Figure 6 are computed according to (43), (47) and (48) and the ISQP algorithm. The results show that the lower bound on secrecy capacity increases with power allocation, implying the high effectiveness of the ISQP algorithm. The secrecy capacity increases with SNR for \(\mathbf{H}\); that is, a low noise level improves secrecy capacity. The lower bound on the secrecy capacity of SCO\(-\)AN decreases when the effect of active eavesdropper is taken into consideration. The lower bound on the secrecy capacity with MMSE channel estimation is greater than the lower bound on that with LS channel estimation. Figure 7 shows SCO\(-\)AN's secrecy capacity with active eavesdropper versus different \(P_{E}\). The effects of different kinds of channel estimation are also considered. The power of \(P_{E}\) increases from 1 to 10, while the \(P_{B}\) is unchanged. ISQP allocates all the transmission power. The results show that the lower bound on secrecy capacity increases with power allocation, implying the high effectiveness of the ISQP algorithm. The secrecy capacity decreases with \(P_{E}\); that is, a low \(P_{E}\) improves secrecy capacity. The lower bound on the secrecy capacity with MMSE channel estimation is greater than the lower bound on that with LS channel estimation. Figure 5: SCO\(-\)AN’s Secrecy Capacity Before and After Power Allocation versus Different SNR and Channel Estimation Algorithm. Figure 6: SCO\(-\)AN’s Secrecy Capacity Before and After Power Allocation versus Different SNR and Channel Estimation Algorithm with and without active eavesdropper. Figure 7: SCO\(-\)AN’s Secrecy Capacity Before and After Power Allocation versus Different \(P_{E}\) and Channel Estimation Algorithm.