# Performance Analysis of Ambient Backscatter NOMA Systems
## 5 Numerical Results
We found that the outage behavior of AmBC-NOMA systems gradually deteriorates as the residual interference value increases: this is because the SNR received by the user decreases as the SNR increases and decoding becomes difficult; therefore, the impact of ipSIC is crucial to the performance of AmBC-NOMA systems. Figure 3: OP at different residual interference versus the transmitting SNR, with \(\varpi=1\), \(\beta=0.17\). Figure 2: OP versus the transmitting SNR, with \(R_{1}=0.4\), \(R_{2}=0.5\), \(R_{3}=0.6\), \(R_{BD}=0.009\) BPCU, and \(\beta=0.17\). Figure 4 plots the OP for three users and the BD at different threshold rates versus the transmitting SNR. We observed that by increasing the threshold rate, the outage behavior of the AmBC-NOMA systems became worse. This was consistent with the characteristics of the traditional NOMA network. Figure 5 illustrates the throughputs of different users and the BD versus the transmitting SNR in the delay-limited transmission mode. The throughput curves were plotted according to (23). The figure demonstrates that as the transmitting SNR rose, the throughputs of the users and the BD gradually increased and converged to a constant, which was due to the self-interference generated by ipSIC and backscattered link interference that limited the performance of the AmBC-NOMA systems. Compared to traditional AmBC-OMA systems, AmBC-NOMA achieves greater system throughput. For example, the throughput of intermediate users gradually  
Figure 6 plots the throughputs of different users and the BD for different reflection coefficients \(\beta\) versus the transmitting SNR. It is evident from the figure that the throughput of the user decreased significantly with the increasing reflection coefficient. With \(\beta\) rises, the outage behavior of the user deteriorated. As a result, the reflection coefficient \(\beta\) reduced the throughput of the system. Figure 7 plots the EE of the users and the BD at different \(\kappa\) versus the transmitting SNR with \(P_{S}\) = 5 dBw, \(P_{UE,d}\) = 10 dBm, \(P_{BS}(d)\) = 10 dBw, and \(P_{BD}\) = 10 dBm. The EE curves were plotted according to (26). According to this figure, the curve of the AmBC-NOMA systems' EE with pSIC was higher than that of the AmBC-OMA systems. In contrast to existing work on backscatter, we considered the adverse effects of ipSIC on the performance of the AmBC-NOMA system. Furthermore, it was indicated that the curve of the AmBC-NOMA systems' EE in the case of ipSIC was lower than that of the AmBC-OMA systems. This was because the system was subject to greater interference with ipSIC. The AmBC-NOMA systems' EE steadily declined as \(\kappa\) rose, which was due to a decrease in the EE of the transmitting amplifier, and a rise in energy usage. Figure 6: Throughput at different reflection coefficient \(\beta\) versus the transmitting SNR, with \(R_{1}\) = 0.4, \(R_{2}\) = 0.5, \(R_{3}\) = 0.6, and \(R_{BD}\) = 0.009 BPCU. Figure 7: EE at different \(\kappa\) versus the transmitting SNR, with \(P_{S}\) = 5 dBw and \(P_{UE,d}\) = 10 dBm. Figure 8 plots the EE of different users and the BD at different hardware damage coefficients versus the transmitting SNR. The results show that the EE curve of the AmBC-NOMA systems decreased with increasing hardware damage factors. This can be explained by a rise in the hardware damage factor, which led to worse outage behavior for both the user and the BD, as well as to a decrease in EE for the AmBC-NOMA systems. In addition, we considered the energy efficiency of the AmBC-NOMA systems with ipSIC. We observed that the energy efficiency of the AmBC-NOMA systems with pSIC was higher than in the case of ipSIC. This was because the SNR received by the user with ipSIC was lower than the SNR received with pSIC, so decoding became difficult. Therefore, the impact of ipSIC on the performance of the system is worth considering.