RINGS: Resilient Wireless Systems for Future Uplink Traffic through Cell-Free, Loosely Coordinated Access

现有的无线系统被设计为支持人工生成的流量，这主要是一个单一的类，有几个主要的应用程序，如语音电话，互联网浏览和视频流。当前蜂窝系统的成功在很大程度上是由于设计了用于将数据从基站传送到用户设备的高效的基于连接的下行链路。这是由于我们对服务质素没有严格要求。实际上，设计度量集中在峰值/平均下行链路数据速率和聚合数据吞吐量上，而不是集中在对单个用户或弹性的强统计保证上。两个因素可能会颠覆无线系统设计理念的现状：不断变化的流量概况和对弹性的关注，而不是平均性能指标。无线流量的增长将来自分布式内容创建者、大量支持分布式学习的无人值守机器、移动的机器人和视频监控设备。这些设备将需要从用户设备到基站的基本上鲁棒且高效的上行链路。此外，这些设备往往会生成丰富多样的流量配置文件和需求配置文件，从而产生对适应性和弹性基础设施的需求。考虑到这一点，该项目解决了需要优化和提高异构设备的上行链路无线接入的鲁棒性。从广泛的角度来看，该项目旨在加强无线网络基础设施。它提供了与业界合作的机会，以便将技术转移到无线标准。与此同时，该项目还致力于创建教育材料，以培养具有全球竞争力的科学、技术、工程和数学（STEM）劳动力，并为无线行业的从业者提供技能发展机会。从技术角度来看，该项目考虑了无单元多输入多输出（MIMO）的范例作为提高蜂窝系统的弹性的解决方案的具有分布式信号处理的系统。它探讨了无源多址接入作为一种手段，以减少在物理和介质访问控制层的协调，从而使一个有效的上行链路内的无小区的范例操作。它寻求创新的解决方案，必须解决的几个关键问题，以设计一个强大的上行链路的基础上无小区无源随机接入与多个发射和接收天线，特别是使分布式学习。本项目所要解决的问题可以分为三个相互关联的类别：（i）无小区MIMO系统中非协调、非正交多址的编码和接收机信号处理算法的设计;（ii）用于联邦学习和边缘计算的新型联合压缩、编码和多址算法的设计;以及（iii）射频（RF）前端和基带信号处理的联合设计，用于减轻由多频带接收器引起的非线性，所述多频带接收器是未来设备的象征。所提出的解决方案方法是基于最近有希望的结果，利用多址接入和稀疏恢复之间的联系，空中联合学习，基于机器学习的新RF前端设计，以及结合基于模型的信号处理和机器学习。解决方案方法的某些方面也是基于多终端信息理论和无比率代码的经典和高效结果，但用于分布式学习的背景下。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Existing wireless systems are designed to support human-generated traffic, which is predominantly a monolithic class with a few dominating applications such as voice telephony, Internet browsing, and video streaming. The success of current cellular systems is in large part due to the design of an efficient connection-based downlink for transferring data from the base station to the user equipment. This has been facilitated by the fact that we have not placed stringent demands on the quality of service. Indeed, design metrics have centered around peak/average downlink data rates and aggregate data throughput rather than on strong statistical guarantees for individual users or resiliency. Two factors are likely to upend the status quo in the design philosophy of wireless systems: the changing profile of traffic and the focus on resilience rather than on average performance measures. Growth in wireless traffic will come from distributed content creators, a massive number of unattended machines enabling distributed learning, mobile robots, and video monitoring devices. These devices will demand a substantially robust and efficient uplink from the user equipment to the base station. In addition, these devices tend to generate a rich diversity of traffic profiles and demand profiles, thereby creating the need for adaptable and resilient infrastructures. With this in mind, this project addresses the need to optimize and improve the robustness of uplink wireless access for heterogeneous devices. From a broad perspective, the project seeks to strengthen wireless network infrastructure. It provides opportunities for collaboration with industry for technology transfer to wireless standards. Concurrently, the project seeks to create educational materials that contribute to the training of a globally competitive science, technology, engineering, and mathematics (STEM) workforce and offers skill-development opportunities to practitioners in the wireless industry.From a technical viewpoint, the project considers the paradigm of cell-free multiple input multiple output (MIMO) systems with distributed signal processing as a solution to improve resiliency of cellular systems. It explores unsourced multiple access as a means to reduce coordination at the physical and medium access control layers, thereby enabling the operation of an efficient uplink within the cell-free paradigm. It seeks innovative solutions to several key problems that must be addressed in order to design a robust uplink based on cell-free unsourced random access with multiple transmit and receive antennas, especially to enable distributed learning. The problems tackled in this project can be grouped into three interrelated categories: (i) the design of codes and receiver signal processing algorithms for uncoordinated, non-orthogonal multiple access in cell-free MIMO systems; (ii) the design of novel joint compression, coding and multiple access algorithms for federated learning and edge computing; and (iii) the joint design of radio-frequency (RF) front ends and baseband signal processing for mitigating non-linearities that result from multiband receivers that are emblematic of future devices. The proposed solution methodologies are based on promising recent results leveraging connections between multiple access and sparse recovery, over the air federated learning, new RF front end designs based on machine learning, and combining model-based signal processing and machine learning. Some aspects of the solution methodologies are also based on classical and highly-effective results in multi-terminal information theory and rateless codes, but used in the context of distributed learning.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.