A Survey on Resource Scheduling and Optimization for Space‑Air‑Ground Integrated Communication‑Sensing‑Computation Networks

Abstract

The development of sixth generation (6G) communications is aimed at achieving global connectivity, covering oceans and remote areas with limited ground infrastructure. With the integration of sensing, communication and computing (ISCC) technology, the space-air-ground integrated network (SAGIN) is evolving from a simple data transmission channel to an intelligent task processing platform. This paper uses a narrative review method to analyze the evolution path of resource orchestration technology in heterogeneous environments, and focuses on mathematical programming and data-driven intelligent methods to optimize resources. The existing research is classified according to the method system. This paper also examines the architecture paradigm, multi-dimensional resource modeling and algorithm system. Studies have shown that static methods such as Lagrangian dual decomposition and block coordinate descent provide the necessary performance benchmarks, and deep reinforcement learning can achieve fast response in high mobility links. In addition, this paper discusses the potential of large language model (LLM) as a semantic interpreter, which can accelerate the process of parsing task requirements into technical constraints in specific applications. This study provides a methodological reference for achieving task-centered connectivity, and believes that a hybrid framework that combines the accuracy of deterministic methods and the adaptability of learning methods is a feasible development direction for future space infrastructure.