Collaborative Research: U.S.-Ireland R&D Partnership: CIF: AF: Small: Enabling Beyond-5G Wireless Access Networks with Robust and Scalable Cell-Free Massive MIMO

合作研究：美国-爱尔兰 R

• 批准号: 2322191
• 负责人: Arnold Swindlehurst
• 金额: $ 30万
• 依托单位: University of California-Irvine
• 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 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322191&HistoricalAwards=false
• 关键词: Collaborative; Research; U.S.; Ireland; R&amp

In wireless cellular networks, interference between user signals in adjacent cells remains a persistent problem that hinders connectivity at the cell boundaries. A new architecture for wireless networks is emerging that eliminates the concept of cells and relies on a distributed network of access points across a wide area that share data and cooperate to combat interference. This new ``cell-free'' approach has been shown to provide significantly more uniform coverage and data rates comparable to, and in some cases better than, the classical centralized cellular approach. To date, most theoretical studies of cell-free systems have assumed relatively favorable radiowave propagation that leads to good performance using simple linear signal processing methods. This project, by contrast, focuses on less favorable scenarios that require more complicated non-linear statistical methods. The research under the project will be conducted with collaborators from U.S., Irish, and Northern Irish institutions, and will support a diverse cohort of postdocs, PhD, Master's, and undergraduate students in key 5G and 6G technologies. The primary goals of this research are to develop nonlinear statistical inference methods for estimation and detection and devise robust and scalable resource allocation solutions for cell-free massive MIMO under less favorable channel propagation conditions. Project research activities will advance the current state of knowledge on cell-free massive MIMO and are organized into three interconnected thrusts: i) Developing nonlinear variational Bayesian methods for centralized and distributed processing, focusing on channel estimation, data detection, and user activity detection tasks; ii) Developing novel user-centric pilot designs and power control schemes, considering practical challenges in cell-free massive MIMO such as hardware impairments and fronthaul quantization errors; and iii.) Devising robust and scalable resource allocation strategies to resolve the user congestion issue and improve network-wide energy efficiency, considering non-uniform data traffic load conditions. The project's demonstration of the capabilities of robust and scalable cell-free massive MIMO will inspire collaborative and cross-disciplinary research efforts and establish cell-free massive MIMO as a transformative beyond-5G/6G wireless access technology.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.

在无线蜂窝网络中，相邻小区中的用户信号之间的干扰仍然是阻碍小区边界处的连接的持续存在的问题。一种新的无线网络架构正在出现，它消除了小区的概念，并依赖于大范围内共享数据并合作对抗干扰的分布式接入点网络。这种新的“无蜂窝”方法已被证明可以提供明显更均匀的覆盖范围和数据速率，与传统的集中式蜂窝方法相当，在某些情况下甚至更好。迄今为止，大多数无细胞系统的理论研究都假设相对有利的无线电波传播，从而使用简单的线性信号处理方法获得良好的性能。相比之下，该项目侧重于需要更复杂的非线性统计方法的不太有利的场景。该项目的研究将与来自美国、爱尔兰和北爱尔兰机构的合作者进行，并将为 5G 和 6G 关键技术领域的各类博士后、博士、硕士和本科生提供支持。本研究的主要目标是开发用于估计和检测的非线性统计推断方法，并为不太有利的信道传播条件下的无小区大规模 MIMO 设计稳健且可扩展的资源分配解决方案。项目研究活动将推进无小区大规模 MIMO 的知识现状，并分为三个相互关联的主旨： i) 开发用于集中式和分布式处理的非线性变分贝叶斯方法，重点关注信道估计、数据检测和用户活动检测任务; ii) 开发新颖的以用户为中心的导频设计和功率控制方案，考虑无小区大规模 MIMO 中的实际挑战，例如硬件损伤和前传量化误差； iii.) 考虑不均匀的数据流量负载条件，制定稳健且可扩展的资源分配策略，以解决用户拥塞问题并提高全网络能源效率。该项目展示了强大且可扩展的无蜂窝大规模 MIMO 的功能，将激发协作和跨学科研究工作，并将无蜂窝大规模 MIMO 确立为一种变革性的超 5G/6G 无线接入技术。该奖项反映了 NSF 的法定使命和通过使用基金会的智力价值和更广泛的影响审查标准进行评估，该项目被认为值得支持。