## 2 Background and Key Features
### UAV-Assisted SAGINs
#### 2.1.3 Key Issues of UAV-Assisted SAGINs  
On one hand, research on 6G-SAGINs focuses on addressing protocol compatibility and seamless handover performance among heterogeneous network components within a framework integrating UAV-as-a-Service (UaaS) and computational networks. On the other hand, while advancing ultra-low latency and ultra-high reliability transmission through resource allocation optimization, challenges such as frequent access point handovers and link anti-interference issues intensify. In summary, the key challenges can be primarily categorized into three aspects, (1) hierarchical architecture, (2) dynamic characteristics, and (3) extreme environments, with current research hotspots including the following:  
1. _Cross-Layer Protocol Adaptation:_ Protocol heterogeneity introduces complexity to SAGIN systems. The traditional TCP attributes packet loss to congestion, leading to performance degradation in high-bit-error-rate SAGIN scenarios. In contrast, the Licklider Transmission Protocol in Delay Tolerant Networking (DTN) employs a delay-based acknowledgment mechanism to distinguish packet loss causes and optimize transmission efficiency [21]. 2. _Network Virtualization and Service Architecture:_ Virtualized service architectures are considered baseline technologies for integrating SAGINs. By abstracting UAVs' and satellites' computational, storage, and communication resources into dynamically allocatable virtual resource pools, they enable on-demand service deployment and task offloading [22]. However, virtualized SAGINs still face challenges such as dynamic topology management, cross-domain security, and additional migration costs and control overhead introduced by virtualization. 3. _Network Resource Awareness:_ Unknown topologies increase the complexity of resource discovery. Multi-source sensors collect data on location, signal strength, energy consumption, and link quality, while vision, radar, and radio frequency identification (RFID) technologies enhance environmental perception. 4. _Topology Prediction:_ Aimed at forecasting future topology changes based on historical and real-time data while dynamically sensing current states, topology prediction is a performance bottleneck for path planning and deployment tasks [5]. Common in UAV swarm ad hoc networks and constellation topology prediction, current approaches include machine learning (ML)-based and physics-based models, categorized as offline or online depending on the prediction methods. 5. _Dynamic Traffic Load:_ For traffic scheduling, SAGINs employ centralized and distributed modes. SDN-like architectures enable global traffic path optimization with controlled latency and load [23]. SDN also allows virtual network resource parti  
Figure 3: Analysis of frequency band allocation in SAGINs. Different colors are used to distinguish adjacent frequency bands. StE1: space to earth; EtS2: earth to space. tioning based on service requirements, prioritizing critical services (e.g., allocating dedicated satellite bandwidth for emergency communications during disasters). * _Resilient Security:_ Adaptive security mechanisms enhance SAGIN availability in dynamic topologies. Dynamic defenses include Moving Target Defense, which periodically alters node IPs, ports, and encryption keys to impede attackers [24], and Zero-Trust Architecture, which enforces continuous authentication and authorization for cross-layer access requests [25]. AI-driven anomaly detection integrated with SDN controllers isolates malicious nodes. Additionally, channel attacks [26; 27] on UAVs remain one of the key challenges in dynamic conditions. * _Endurance and Efficiency:_ The short endurance of small UAVs (commercial UAVs typically last less than 2 h) and long-term hovering requirements for HAPs are critical technical barriers. Hybrid hydrogen fuel cells and solar energy offer viable solutions [17], exemplified by Google Loon's solar-powered balloons achieving months-long flight.