Device-satellite-satellite collaborative task offloading computing and resource allocation in 6G satellite-ground edge computing network

The popularity of smart devices extends emerging intelligent applications to remote areas. The limited computing capacity of devices and inadequate ground computing facilities make it a challenge to efficiently process computation-intensive tasks. Fortunately, satellite edge computing networks can provide powerful computing services for users in remote areas. However, the mobility of satellites and the spatio-temporal characteristics of networks bring great challenges to multi-satellite collaborative computing. To address these challenges, we propose a device-satellite-satellite collaborative edge computing network that jointly optimizes task offloading decision and ratio, bandwidth and computing resource allocation. Since traditional optimization algorithms cannot handle time-varying NP-hard problem, we propose an intelligent task offloading method based on multi-agent twin delayed deep deterministic policy gradient. To address the high algorithm complexity brought by high-dimensional action space, we decompose the joint optimization problem into task offloading subproblem and computing resource allocation subproblem. We combine the Lagrangian optimization method to establish a satellite computing resource allocation mechanism, which is convenient for satellite to quickly allocate its edge computing capability according to task requirements, and reduces the dimension of the agent action space. Finally, we propose an Intelligent Task Offloading and Lagrange Optimization Assisted Resource Allocation (ITO-LOARA) algorithm to maximize the task success rate and minimize the task execution delay and energy consumption. Simulation results show that our proposed scheme achieves efficient task offloading and collaborative computing between ground devices and satellite edge computing nodes, and has superior performance, better scalability, and higher stability compared with the baseline and mainstream algorithms.