Cloud Assisted Two-Tier Wireless Networks

云辅助两层无线网络

• 批准号: RGPIN-2021-04298
• 负责人: Sousa, Elvino
• 金额: $ 2.4万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751660
• 关键词: Cloud; Assisted; Two; Tier; Wireless

This project will address a new concept for public wireless cellular networks based on the Two-Tier concept that we have been proposing to the industry. This concept will target beyond 5G networks and we utilize data driven neural network and machine learning methodologies for network configuration, interference management, and resource allocation. The Two-Tier concept involves an architecture with the regular base stations which we refer to as primary nodes, secondary nodes that are installed close to the user terminals which act in a sense like relays but are quite different from the relays studied in the current literature, and the user terminals. The link from the primary nodes to the secondary nodes is referred to as the primary link and it is the focus of this project. The secondary nodes will have the capability for a larger number of antenna elements than is possible in a regular user terminal, and the link from the primary nodes to the secondary nodes will be well-behaved in terms of channel propagation and prediction. The focus of the proposal is to devise data driven techniques to optimize this link. There are two main focus cases: 1) The secondary nodes are stationary, and 2) The secondary nodes are placed in a moving platform such as a car, truck, bus, streetcar, or train. The project will focus on novel techniques to associate a channel with a time and location so that we can do much more efficient physical layer adaptation than in normal cellular schemes. The techniques will involve the use of cloud databases to contain information about the propagation environment in relation to the location of the secondary node, either in a fixed installation such as a home or office, or in a moving platform along a street. The techniques utilized in the project will involve neural networks and machine learning in order to do power level adaptation, antenna beam configurations, and co-operation between primary nodes. The ultimate goal in this research is to reduce the radiation footprint of transmitters significantly so as to enhance network capacity, increase physical layer security, decrease energy consumption, and minimize radiation.  In our research we will develop an architecture that complements the traditional approaches of small-cells and massive MIMO on traditional base stations. The one-tier small cell approach has the drawback of requiring a back-haul network to interconnect the small cells, and the massive MIMO approach used on conventional base stations has the drawback that when we have a large number of terminals such as in IoT applications even the massive MIMO approach will have limitations due to excessive overhead. Our approach will complement these two classical approaches and allow for custom network configuration implementations that are specific to a City and a network operator.

该项目将根据我们一直向行业提出的两层概念来解决公共无线蜂窝网络的新概念。这个概念将针对5G网络以外的目标，我们利用数据驱动神经网络和机器学习方法进行网络配置，干扰管理和资源分配。两层概念涉及一个带有常规基站的体系结构，我们称为主要节点，即接近接力线的用户终端附近安装的次级节点，但与当前文献中研究的继电器和用户终端截然不同。从主要节点到辅助节点的链接称为主要链接，这是该项目的重点。次级节点将具有比常规用户终端相比的更大天线元素的能力，并且从主要节点到辅助节点的链接将在通道传播和预测方面得到很好的表现。该提案的重点是设计数据驱动技术以优化此链接。有两个主要的焦点案例：1）次级节点是固定的，2）次级节点放在移动的平台中，例如汽车，卡车，公共汽车，有轨电车或火车。该项目将集中在新型技术上，将通道与时间和位置相关联，以便我们可以比正常的蜂窝方案进行更有效的物理层适应性。这些技术将涉及使用云数据库来包含有关二级节点位置的传播环境的信息，无论是在固定安装（例如家庭或办公室）中，或在街道上移动的平台中。项目中使用的技术将涉及神经网络和机器学习，以进行功率水平适应性，天线束配置以及主要节点之间的合作。这项研究的最终目标是大大减少发射机的辐射足迹，以增强网络容量，提高物理层安全性，降低能源消耗并最大程度地减少辐射。在我们的研究中，我们将开发一种体系结构，该体系结构符合传统基站在传统基站上的传统方法。单层小细胞方法的缺点是需要一个后空网络互连小单元，并且在常规基站上使用的大量MIMO方法的缺点是，当我们拥有大量终端时，例如在IoT应用中（即使是IOT应用程序），甚至大规模的MIMO方法都会由于超高的头顶而具有限制。我们的方法将完成这两种经典方法，并允许定制的网络配置实现，这些实现是针对城市和网络运营商的。