SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond
SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond
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DOI：
10.1109/access.2020.2991959
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Syed Muhammad Asad Zaidi;Marvin Manalastas;H. Farooq;A. Imran
中科院分区：
文献类型：
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作者：
Syed Muhammad Asad Zaidi;Marvin Manalastas;H. Farooq;A. Imran
蜂窝系统设计向5G及以后的快速发展，需要在各种部署和用例场景的实际设置中研究新功能、设计建议和解决方案。虽然目前存在许多4G和5G的系统级模拟器，但特别需要一个符合3GPP标准的系统级整体和现实的模拟器，以支持对文献中提出的大量基于人工智能的网络自动化解决方案的评估。在本文中，我们介绍了AI4networks实验室开发的这样一个模拟器，称为SyntheticNET。据作者所知，SyntheticNET是第一个完全符合3GPP 5G标准第15版的基于python的模拟器，并且可以升级到未来的版本。与现有的模拟器相比，SyntheticNET的主要特点包括：1)模块化结构，便于交叉验证和升级到未来版本；2)基于测量、光线追踪或AI的灵活传播建模；3)能够导入基于实际供应商特定基站特征（如天线和能耗模式）的测量数据表；4)支持基于5G标准的自适应命理学；5)从实际地理地图中得出的切合实际和用户特定的移动模式；6)详细交接（HO）流程实施；结合数据库辅助边缘计算。SyntheticNET的另一个关键特性是它可以轻松地用于测试基于人工智能的网络自动化解决方案。作为第一个基于python的5G模拟器，这种易用性部分源于SyntheticNET的内置处理和分析大型数据集的能力，以及对机器学习库的集成访问。因此，SyntheticNET模拟器为学术界和工业界提供了一个强大的平台，不仅可以研究优化设计，部署和操作现有和新兴蜂窝网络的新解决方案，还可以在未来实现人工智能支持的深度自动化。
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.