Multi-Domain Resource Management for Space-Air-Ground Integrated Sensing, Communication, and Computation Networks

To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.