# Design of JiuTian Intelligent Network Simulation Platform

arXiv: https://arxiv.org/abs/2310.06858 · HTML: https://mlsanity.com/paper/2310.06858/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [AI for 5G: Research Directions and Paradigms](https://mlsanity.com/paper/1807.08671/) (2018)

## Built on by (most cited first, 2 of 2)

- [Emulators in JINSP](https://mlsanity.com/paper/2311.16146/) (2023)
- [Implement services for business scenarios by combining basic emulators](https://mlsanity.com/paper/2312.08815/) (2023)

## Abstract (arXiv)

This paper introduced the JiuTian Intelligent Network Simulation Platform, which can provide wireless communication simulation data services for the Open Innovation Platform. The platform contains a series of scalable simulator functionalities, offering open services that enable users to use reinforcement learning algorithms for model training and inference based on simulation environments and data. Additionally, it allows users to address optimization tasks in different scenarios by uploading and updating parameter configurations. The platform and its open services were primarily introduced from the perspectives of background, overall architecture, simulator, business scenarios, and future directions.