Collaborative Research: CNS Core: Small: Adaptive Smart Surfaces for Wireless Channel Morphing to Enable Full Multiplexing and Multi-user Gains

合作研究：CNS 核心：小型：用于无线信道变形的自适应智能表面，以实现完全复用和多用户增益

• 批准号: 2343959
• 负责人: Alireza Vahid
• 金额: $ 17万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2343959&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

The connectivity solutions need to scale up massively to support unprecedented exponential growth in data rates and the number of devices. The primary approach to meet the demands has been to upgrade the infrastructure, such as adding base stations with multiple antennas and/or upgrading clients. Unfortunately, these approaches have fallen short of their goal as in practice, the channels between users and base stations have limited scattering or richness to enable spatial multiplexing, and users are not sufficiently separated in the spatial domain. This proposal will shape the channel to unlock the full potential of wireless networks without requiring updating the base station or user device at a low cost in terms of power and expense. The proposed research aims to adaptively modify and morph the wireless medium using smart passive reflector surfaces to materialize the spatial multiplexing gains. The underlying idea is to use smart reflectors to create additional channel diversity to manipulate the received signal before reflecting it to minimize interference at the receivers. The research brings together PIs and techniques from communications theory, networked system design, and antenna design to build scalable and low-cost wireless networks. The project also proposes a synergistic educational and outreach plan that leverages the technical work to build exciting demos for undergraduate and K-12 classrooms. For example, a smart surface-based demo, which increases the data rate and coverage of current Wi-Fi devices, will introduce students to the wonders of engineering in a pragmatic yet compelling way, with the hope of increasing diversity in STEM-related education environments.The proposal would develop smart surface designs capable of morphing the channel, and develop the hardware prototype and algorithms to provide coverage and multiplexing gains to support massive numbers of users and the exponential increase in data rates. The designed smart surface with fine-grained control would allow us to morph the wireless channel or environment to increase the diversity and multiplexing gains as deemed necessary during optimization. We further establish smart surface placement theory and algorithms and quantify the performance-size trade-offs. We develop low-overhead channel estimation and low-latency optimization algorithms for the base station to support temporal channel changes and improve the data rate and connectivity. The proposed effort would develop the theory and algorithms for multi-user setups to ensure scalability with smart surfaces. The proposed work would enable the first low-cost, practical smart surface and algorithms supporting high-mobility users with significant data-rate improvements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

连接解决方​​案需要大量扩展，以支持数据速率和设备数量的前所未有的指数增长。满足需求的主要方法是升级基础架构，例如使用多个天线和/或升级客户端添加基站。不幸的是，这些方法在实践中未达到其目标，用户和基站之间的渠道具有有限的散射或丰富性，可以实现空间多路复用，并且用户在空间域中没有足够的分离。该提案将塑造通道，以解锁无线网络的全部潜力，而无需以低成本在功率和费用方面更新基站或用户设备。拟议的研究旨在使用智能无源反射器表面自适应修改和变形无线介质，以实现空间多路复用的增长。潜在的想法是使用智能反射器创建其他频道多样性，以在反射后将接收的信号操纵信号以最大程度地减少接收器的干扰。该研究汇集了通信理论，网络系统设计以及天线设计的PI和技术，以构建可扩展和低成本的无线网络。该项目还提出了一项协同的教育和外展计划，该计划利用技术工作为本科和K-12教室建立令人兴奋的演示。例如，一个基于智能表面的演示增加了当前Wi-Fi设备的数据速率和覆盖范围，它将以一种务实而引人注目的方式向学生介绍工程的奇观，希望增加与STEM相关的教育环境中的多样性，该建议将在与STEM有关数据速率的指数增加。具有细粒度控制的设计智能表面将使我们能够将无线通道或环境变形，以增加在优化过程中所必需的多样性和多路复用增长。我们进一步建立智能表面放置理论和算法，并量化性能大小的权衡。我们为基站开发低空的通道估计和低延迟优化算法，以支持时间通道更改并提高数据速率和连接性。提出的努力将开发多用户设置的理论和算法，以确保使用智能表面可扩展性。拟议的工作将使第一个低成本，实用的智能表面和算法能够通过大量的数据率改进来支持高动力使用者。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来通过评估来提供支持的。