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A Study on Deep Learning Based Resource Allocation for Future Cellular Networks

基于深度学习的未来蜂窝网络资源分配研究

基本信息

批准号：
20K19777
负责人：
毛伯敏
金额：
$ 2万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Early-Career Scientists
财政年份：
2020
资助国家：
日本
起止时间：
2020-04-01 至 2021-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K19777/
关键词：
Resource allocation AI 6G Network optimization Deep learning Artificall Intelligence Cellular Network

项目摘要

With the significantly improved transmission rate and network capacity for 5G and even the expected 6G in 2030, an increasing number of end terminals will be connected to the Internet to serve the users. And the emerging network services are expected to have user-aware or content-aware requirements for various metrics including delay, security, packet loss rate, and so on. To meet the stringent and diversified service requirements, the network resource needs to be allocated by more advanced methods than conventional mathematical models due to the extreme complexity and frequent dynamics. The Artificial Intelligence (AI) methods, especially the state-of-the-art deep learning technologies, have been regarded as the only method since it can tackle the extremely complex problems efficiently and flexibly. And AI-based network management has been regarded as the network paradigm toward 6G. The purpose of this project is to study the AI-based resource allocation for future cellular networks.In the first year of this project, the existing research has been widely surveyed and studied. And some mature results have been obtained and published on and submitted to the high impact factor IEEE journals/transactions. The AI techniques have been demonstrated to be effective for intelligently allocate the network resource for cellular networks.

随着2030年5G乃至预期的6G传输速率和网络容量的显著提高，越来越多的终端将连接到互联网上服务于用户。而新兴的网络服务预计将对包括延迟、安全性、丢包率等在内的各种度量具有用户感知或内容感知的要求。由于网络资源的极端复杂性和频繁的动态变化，为了满足日益苛刻和多样化的业务需求，需要采用比传统数学模型更先进的方法来分配网络资源。人工智能(AI)方法，特别是最新的深度学习技术，因其能够高效、灵活地处理极其复杂的问题而被认为是唯一的方法。基于人工智能的网络管理被认为是6G的网络范式。本项目的目的是研究基于人工智能的未来蜂窝网络资源分配问题。在本项目的第一年，对现有的研究进行了广泛的调查和研究。一些成熟的成果已经发表在高影响因子的IEEE期刊/会刊上并提交给了它们。人工智能技术已被证明是为蜂窝网络智能分配网络资源的有效方法。