SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

Syed Muhammad Asad Zaidi; Marvin Manalastas; Hasan Farooq; Ali Imran

doi:10.1109/ACCESS.2020.2991959

IEEE Access (Jan 2020)

SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

Syed Muhammad Asad Zaidi,
Marvin Manalastas,
Hasan Farooq,
Ali Imran

Affiliations

Syed Muhammad Asad Zaidi: ORCiD; Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
Marvin Manalastas: Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
Hasan Farooq: Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
Ali Imran: Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA

DOI: https://doi.org/10.1109/ACCESS.2020.2991959
Journal volume & issue: Vol. 8
pp. 82938 – 82950

Abstract

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The rapid evolution of cellular system design towards 5G and beyond gives rise to a need for investigation of the new features, design proposals and solutions in realistic settings for various deployments and use case scenarios. While many system level simulators for 4G and 5G exist today, there is particularly a dire need for a 3GPP compliant system level holistic and realistic simulator that can support evaluation of the plethora of AI based network automation solutions being proposed in literature. In this paper we present such a simulator developed at AI4networks Lab, called SyntheticNET. To the best of authors' knowledge, SyntheticNET is the very first python-based simulator that fully conforms to 3GPP 5G standard release 15 and is upgradable to future releases. The key distinguishing features of SyntheticNET compared to existing simulators include: 1) a modular structure to facilitate cross validation and upgrading to future releases; 2) flexible propagation modeling using measurement based, ray tracing based or AI based propagation modeling; 3) ability to import data sheet based on measurement based realistic vendor specific base station features such as antenna and energy consumption pattern; 4) support for 5G standard based adaptive numerology; 5) realistic and user-specific mobility patterns that are yielded from actual geographical maps; 6) detailed handover (HO) process implementation; and 7) incorporation of database aided edge computing. Another key feature of the SyntheticNET is the ease with which it can be used to test AI based network automation solutions. Being the first python based 5G simulator, this ease, in part stems for SyntheticNET's built-in capability to process and analyze large data sets and integrated access to Machine Learning libraries. Thus, SyntheticNET simulator offers a powerful platform for academia and industry alike to investigate not only new solutions for optimally designing, deploying and operating existing and emerging cellular networks but also for enabling AI empowered deep automation in the future.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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