Synthetic NET: An AI-Enabled 5G and Beyond 3GPP Compliant Simulator

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Dr. Anitha Patil, Shaik faizan, Wajahat Ali Khan, Syed Shoaib Ahmed, Shaik Shoaib Ahmed


New features, design suggestions, and solutions for the next generation of cellular systems need to be tested in a variety of real-world deployments and use cases. 3GPP-compliant 3GPPcompliant system-level holistic and realistic simulators are urgently needed to evaluate the variety of AI-based network automation solutions that are being suggested in the literature. The Synthetic-NET simulator created at AI4networks Lab is presented in this publication. A python-based emulator that completely complies with 3GPP 5G standards update 15 and can be upgraded to future releases called Synthetic-NET, according to the authors. In comparison to other simulators, Synthetic-NET has a number of key advantages, including: 1) a modular design that makes it easier to cross-validate and upgrade to future releases; 2) a variety of propagation modelling options, including measurement-based, ray-tracing-based, and AI-based models; 3) the ability to import data sheets based on measurements of realistic base [5] station features, including such satellite and energy consumption patterns; and 4) sui generis support for a wide range of wireless protocols. The Synthetic-NET's ability to be utilised to test AI-based network automation solutions is another important feature of the product. Synthetic Net’s built-in abilities to analyze and process large amounts of data and integrated access to Applications Of machine learning [12] contribute to this simplicity of use, which is the first python-based 5G emulator. A powerful platform for both academia and industry alike, the Synthetic-NET simulator is a powerful tool for experimenting of not only creative approaches for optimising the operation of both existing as well as starting to emerge wireless connections but also for developing AI-powered deep mechanisation in the years to come.


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