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.

该项目将基于我们一直向业界提出的双层概念，提出一个公共无线蜂窝网络的新概念。这一概念将针对5G网络之外的网络，我们利用数据驱动的神经网络和机器学习方法来进行网络配置、干扰管理和资源分配。两层概念涉及具有常规基站的架构，我们将其称为主节点、安装在用户终端附近的次节点和用户终端，次节点在某种意义上像中继器一样起作用，但与当前文献中研究的中继器完全不同。从主节点到次节点的链路称为主链路，是本项目的重点。次节点将具有比常规用户终端中可能的天线元件更大数量的天线元件的能力，并且从主节点到次节点的链路在信道传播和预测方面将表现良好。该提案的重点是设计数据驱动技术，以优化这一联系。有两种主要的焦点情况：1）次级节点是固定的，以及2）次级节点被放置在移动的平台上，例如汽车、卡车、公共汽车、有轨电车或火车。该项目将集中在新的技术，以关联一个信道的时间和位置，使我们可以做更有效的物理层适应比正常的蜂窝计划。该技术将涉及使用云数据库来包含关于与次级节点的位置相关的传播环境的信息，次级节点在诸如家庭或办公室的固定安装中，或者在沿着街道沿着的移动平台中。该项目中使用的技术将涉及神经网络和机器学习，以便进行功率电平自适应，天线波束配置以及主节点之间的合作。本研究的最终目标是显著减少发射机的辐射足迹，从而提高网络容量，增加物理层安全性，降低能耗，并最大限度地减少辐射。在我们的研究中，我们将开发一种架构，以补充传统基站上的小小区和大规模MIMO的传统方法。单层小小区方法具有需要回程网络来互连小小区的缺点，并且在传统基站上使用的大规模MIMO方法具有这样的缺点：当我们具有大量终端时，例如在IoT应用中，即使大规模MIMO方法也将由于过多的开销而具有限制。我们的方法将补充这两个经典的方法，并允许自定义网络配置实现，是特定于城市和网络运营商。