OMNIS: Semantic RAN Slicing via Dynamic Split Neural Networks

Edge computing enables resource-constrained devices to execute machine learning applications via task offloading. To this aim, radio access network (RAN) slicing is instrumental to provide the necessary network resources. However, current RAN slicing approaches rely on static computing models, thereby constraining their ability to leverage the dynamic semantic data representation capabilities enabled by recent neural architectures. In this paper, we propose OMNIS, a semantic RAN slicing framework for edge computing built on dynamic split neural models. OMNIS embeds a dynamic form of neural compression paired with adaptive data encoding for task offloading, enabling flexible communication payload and computing options. We explicitly study the interplay between neural compression and information quantization in computer vision tasks and design a novel "Box" quantization scheme that improves resiliency to bit errors as a function of compression rate. Considering partial observability and differing objectives of mobile devices and the edge server, we formulate neural gate control and resource slicing optimization problems and solve them via multi-agent contextual multi-armed bandits and convex optimization algorithms. Experimental results show that OMNIS improves inference accuracy by up to 22.85% and reduces quality of service violations by up to 10×. The evaluation code is available at https://github.com/qlt315/OMNIS.