Published June 7, 2020 | Version v1
Conference paper Open

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

  • 1. Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Description

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.

Notes

Grant numbers : SPOT5G - Single Point of attachment communications heterogeneous mobile data networks (TEC2017-87456-P), 5G-Solutions - 5G-Solutions (H2020-ICT-2018-3 // Grant agreement ID: 856691) and CONNECT - Innovative smart components, modules and appliances for a truly connected, efficient and secure smart grid (01 April 2017 - 31 March 2020) (737434 project code).© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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