# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 4 Numerical Results
Figure 9 studies the impact of bandwidth \(B\) on the spectrum advantage SA, for \(p_{s}\in\{43,45\}\) dBm, \(p_{u}\in\{13,18,23\}\) dBm, and \(B\in\{2,3,4,5,6\}\)MHz. When both \(p_{u}\leq 18\) dBm and \(p_{s}\geq 42\) dBm, SA \(>0\), which indicates that the FFR strategy outperforms FDMA in AAoI\({}_{adwan}\) per unit spectrum resource. Under the FFR strategy, interference from the UAV-GU channel to the SAT-GU channel can be mitigated by setting \(p_{u}\) and \(p_{s}\) appropriately to fully utilize spectrum resources and improve system throughput. This, in turn, increases the status information update rate and improves the AAoI\({}_{PW}\) and SA. The above analysis informs that in spectrum-constrained scenarios such as rural areas lacking infrastructure, the FFR strategy allows for better utilization of available spectrum, leading to improved system throughput and AoI performance. Moreover, SA gradually decreases with the increase in \(B\). This trend suggests that the advantage of FFR in terms of spectrum efficiency diminishes when the spectrum resource is sufficient. It further informs that FFR is beneficial in scenarios with constrained spectrum. On the other hand, when \(p_{u}>18\) dBm or \(p_{s}<42\) dBm, SA \(<0\). Under the FFR strategy, either small \(p_{s}\) or large \(p_{u}\) leads to significant interference from UAV-GU channel to SAT-GU channel, which deteriorates the system throughput and SA. Although increasing \(B\) improves SA, the benefit is not significant. This is obviously because more spectrum resources have to be allocated. The insights observed from above analysis of SA can provide a reference for spectrum management strategies  
Figure 8: AoI-related energy advantage of FFR strategy over FDMA strategy. in SAGIN. For example, in SAGIN, dynamic adjustment of spectrum allocation based on the current power settings and interference levels can optimize network performance. In practice, this could mean deploying cognitive radio techniques that allow the network to dynamically sense and adapt to varying interference, thereby optimizing spectrum efficiency and improving network performance such as AoI. Based on the comprehensive analysis of Figures 7-9, it can be concluded that optimizing A AoI-related performance requires the judicious selection of transmit power to effectively utilize constrained power and spectrum resources. Moreover, the analysis shows the advantages of FFR in SAGIN and highlights the requirement for adaptive management strategies in SAGIN. For the satellite-UAV synergy remote sensing application in SAGIN, it is necessary to dynamically adjust the transmit power and spectrum allocation according to the real-time network environment to optimize system energy efficiency, spectrum efficiency, AoI, etc., ensuring that quality of service is satisfied while avoiding the waste of resources. The future research could focus on developing intelligent algorithms that leverage machine learning to predict and respond to the complex and dynamic network environment in SAGIN. This would enable more efficient and effective resource management. Furthermore, the analysis highlights the critical role of interference management in SAGIN. It is essential to explore technologies such as beamforming, interference cancellation, and advanced modulation schemes to mitigate the impact of interference, especially in disaster areas, rural areas, and battlefield electromagnetic environment.