Collaborative Edge Computing and Program Caching With Routing Plan in C-NOMA-Enabled Space-Air-Ground Network

Through deploying satellites and unmanned aerial vehicles (UAVs) with onboard processing capability, the space-air-ground edge computing network (SAGECN) is poised to support ubiquitous access and computation offloading for Internet of Things (IoT) terminals deployed in remote areas. However, the current SAGECN faces several challenges in realizing its full potential, such as scarce spectrum resources, diverse computational demands, and dynamic network circumstances. To meet these challenges, we propose a cluster-non-orthogonal multiple access (C-NOMA)-enabled SAGECN model, where a satellite and multiple UAVs act as collaborative edge servers to execute tasks from IoT terminals. Since each offloaded task should be processed via a specific program, the edge servers carry out program caching, whilst transfer the tasks that do not match the cached programs to another server in a multi-hop manner. Considering the delay-sensitive requirements of computation tasks, we formulate a joint task offloading, communication-computation-cache resource assignment, and routing plan problem, aimed at minimizing the average system latency. To cope with this challenging issue, we partition it into three subproblems. First, a multi-agent learning-based approach is developed to collaboratively train the task offloading, flight trajectory, and program caching. As a step further, two optimization subroutines are embedded to perform routing plan, subchannel allocation, and power control, thereby rendering the overall solution. Experimental results reveal that our approach achieves outstanding performance in terms of system delay and spectrum efficiency.