Decentralized and Versatile Edge Encoding Methods for Task-Oriented Communication Systems

In this paper, we develop task-oriented edge networks in which separate edge nodes perform decentralized inference processes with the aid of a cloud. Individual ENs compress their local observations into uplink messages using task-oriented encoder neural networks (NNs). Then, the cloud carries out a remote inference task by leveraging received signals. We develop fronthaul-cooperative DNN architecture along with proper uplink coordination protocols. Inspired by the nomographic function, an efficient cloud inference model becomes an integration of a number of shallow DNNs. This modulized architecture brings versatile calculations that are independent of the number of ENs. Numerical results demonstrate the viability of the proposed method for optimizing task-oriented edge networks.