Satellite edge computing has attracted the attention of many scholars due to its extensive coverage and low delay. Satellite edge computing research remains focused on on-orbit task scheduling. However, existing research has not considered the situation where heavily loaded satellites cannot participate in offloading. To solve this problem, this study first models the task scheduling of dynamic satellite networks as a minimization problem that considers both the weighted delay and energy consumption. In addition, a hybrid genetic binary particle swarm optimization (GABPSO) algorithm is proposed to solve this optimization problem. The simulation results demonstrate that the proposed method outperforms the other three baseline algorithms.

The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computational complexity and low acquisition accuracy under high dynamic conditions. In this paper, a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain is proposed. The algorithm maps the incoming signal of the same interval to multiple carrier frequency bins and overlaps the mapped signal that belongs to the same code phase. Meanwhile, the code domain compression is introduced to the preprocessed signal, replacing circular correlation with compressed reconstruction to obtain Doppler frequency and code phase. Theoretical analyses and simulation results show that the proposed algorithm can improve the frequency search accuracy and reduce the computational complexity by about 50% in high dynamics.

Satellite edge computing (SEC) has garnered significant attention for its potential to deliver services directly to users. However, the uneven distribution of receiving tasks among satellites in the constellation can lead to uneven utilization of computing resources. This paper proposes a task offloading strategy for SEC that aims to minimize the average delay and energy consumption of tasks by assigning them to appropriate satellite nodes. The approach uses Lyapunov optimization to convert the long-term optimization problem with task queue length constraints into an assignment problem within a single time slot and solve it based on the Hungarian algorithm. Experimental simulations have shown that the proposed algorithm performs better than other baseline algorithms.

Satellite edge computing has garnered significant attention as an essential component of 6G. Among various research areas, satellite dynamic task scheduling holds a prominent position. This paper addresses the scenario of satellite dynamic receiving task on-orbit computing and proposes a collaborative approach by offloading the tasks to multiple satellites. To tackle the scheduling problem, a Markov decision process framework is adopted, and a dynamic scheduling algorithm based on the A3C algorithm is proposed. Comparative analysis with three benchmark algorithms reveals that the proposed algorithm excels in reducing both average task delay and satellite load.