OPEX-Limited 5G RAN Slicing: an Over-Dataset Constrained Deep Learning Approach

Abstract

In this paper, we investigate the concept of OPEX-limited resource provisioning as a key component in fifth generation (5G) radio access networks (RAN) slicing. The different RAN slices' tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources and backhaul capacity. To achieve this dynamic resource allocation, we rely on key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes. These datasets are used to train a new class of deep neural networks (DNNs) models where OPEX requirements, formulated as non-convex non-differentiable violation rate constraints, are also dataset-dependent. The designed constrained DNNs are then optimized via a non-zero sum two-player game strategy. In this respect, we highlight the effect of the different hyperparameters on the respect of the OPEX limitations, while ensuring a dynamic RAN resource orchestration that follows the slices' traffics trends.