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

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性的持续问题。一种新的无线网络架构正在出现，它消除了小区的概念，并依赖于跨越广域的接入点分布式网络，这些接入点共享数据并合作对抗干扰。这种新的"无蜂窝"方法已被证明提供了比传统的集中式蜂窝方法更均匀的覆盖范围和数据速率，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，导致使用简单的线性信号处理方法的良好性能。相比之下，本项目侧重于需要更复杂的非线性统计方法的不太有利的情况。该项目下的研究将与来自美国，爱尔兰和北方爱尔兰机构，并将支持关键5G和6G技术的博士后，博士，硕士和本科生的多元化队列。本研究的主要目标是开发非线性统计推断方法的估计和检测，并制定鲁棒性和可扩展的资源分配解决方案，无小区的大规模MIMO在不利的信道传播条件下。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重点：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，重点是信道估计，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;以及iii.）设计鲁棒的和可扩展的资源分配策略，以解决用户拥塞问题，并提高整个网络的能源效率，考虑非均匀的数据流量负载条件。该项目展示了强大且可扩展的无蜂窝大规模MIMO的能力，将激发合作和跨学科的研究努力，并将无蜂窝大规模MIMO确立为变革性的超5G/6G无线接入技术。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响评审标准进行评估，被认为值得支持。

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.