Ultrareliable Low-Latency Slicing in Space-Air-Ground Multiaccess Edge Computing Networks for Next-Generation Internet of Things and Mobile Applications

We study the problem of ultrareliable and low-latency slicing in multiaccess edge computing (MEC) systems for the next-generation Internet of Things (IoT) and mobile applications operating in the space-air-ground integrated network. The network has a dynamic topology formed by multiple nonstationary nodes with unstable communication links and unreliable processing/transmission resources. Each node can be in one of two hidden states: 1) reliable-in which the node generates no data errors and no losses and 2) unreliable-when the node can generate/propagate random data errors/losses. Solving this problem is difficult, as it represents the nondeterministic polynomial-time (NP) hard nonconcave nonsmooth stochastic maximization problem which depends on the unknown hidden nodes' states and private information about local, dynamic parameters of each node, which is known only to this node, and not to other nodes. To address these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states. We then propose a novel algorithm based on the online alternating direction method of multipliers (ADMMs)-an extension of the well-known classical "static" ADMM to dynamic settings, where our slicing problem can be solved distributedly, in real time, without revealing local (private) information of the nodes. We show that our algorithm converges to a global optimum of the slicing problem and has a good consistent performance even in highly dynamic, unreliable scenarios.