Ultra-Low-Latency Feature Transmission for Edge Inference

The sixth-generation (6G) mobile networks will feature the widespread deployment of artificial intelligence (AI) algorithms at the network edge, which provides a platform for edge intelligence. In this paper, we propose a new air-interface framework targeting the edge inference systems, called ultra-low-latency (observation) feature transmission (ULL-FT). It consists of a novel transmission approach that exploits classifier’s robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture (GM) model, we mathematically derive the relation between BEP and classification margin under constraints on classification accuracy and transmission latency. The result sheds light on system requirements to support ULL-FT. Finally, experiments using deep neural networks (DNN) as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on classification accuracy.