NeTS: Small: Collaborative Research: BRICK: Breaking the I/O and Computation Bottlenecks in Massive MIMO Base Stations

大规模多天线（MIMO）无线系统，其中基站配备了数百或数千个天线元件，与现有的蜂窝通信系统相比，将实现前所未有的数据速率、小区覆盖和传输可靠性。然而，数百或数千个射频收发器和天线的存在导致过高的内部数据速率，从而导致芯片互连和计算瓶颈，阻碍了该技术在实践中的直接部署。该项目将开发依赖于基站分散信号处理的新技术，以避免这些瓶颈，从而产生可行且可扩展的解决方案，使基站设计具有数千个天线元件，而不会牺牲性能或可靠性。除了在实践中实现大规模MIMO之外，该项目还将通过与电信行业合作，利用Argos大规模MIMO测试平台和其他网络研究测试平台，推进未来的蜂窝网络。该项目对教育和推广的广泛影响将包括多个组成部分，包括(i)培训不同的学生群体，作为通信理论和电路设计领域合作的多机构研究团队的一部分，（ii）将工作成果整合到本科和研究生课程中，以及（iii）通过OpenStax课件通过公共领域软件包和开放教育资源广泛提供研究成果。该项目为上行链路（用户与基站通信）和下行链路（基站与用户通信）开发了新颖的分散算法以及基于天线集群和并行化的超大规模集成（VLSI）和图形处理单元（GPGPU）架构上的通用计算。分散基带处理的主要思想是将基站的信号处理工作负载划分为多个计算结构，每个计算结构仅连接到射频收发器和天线的一个子集。为了减少芯片互连和计算瓶颈，该项目研究了(i)基于优化的算法，该算法在天线集群之间交换共识信息；（ii）基于消息传递的算法，该算法完全避免了这种共识交换。最有前途的算法解决方案将在现场可编程门阵列和GPGPU集群上实现，以评估开发的解决方案在实际性能、硬件和带宽约束下的有效性和局限性。此分析结果将提供指导方针，以实现使用分散基带处理的最佳大规模MIMO基站设计。

Massive multi-antenna (MIMO) wireless systems, in which the base-station is equipped with hundreds or thousands of antenna elements, will enable unprecedented data rates, cell coverage, and transmission reliability compared to that of existing cellular communication systems. The presence of hundreds or thousands of radio-frequency (RF) transceivers and antennas, however, results in excessively high internal data rates, which results in chip-interconnect and computation bottlenecks that prevent a straightforward deployment of this technology in practice. This project will develop new technologies that rely on decentralized signal processing at the base-station in order to avoid these bottlenecks, which leads to feasible and scalable solutions that enable base-station designs with thousands of antenna elements without sacrificing performance or reliability. In addition to enabling massive MIMO in practice, the project will advance future cellular networks through collaboration with the telecommunications industry, with the Argos massive MIMO testbed, and other network research testbeds. The project's broader impact on education and outreach will include multiple components, including (i) training a diverse group of students as part of a collaborative, multi-institutional research team in the areas of communication theory and circuit design, (ii) integrating outcomes of the work into undergraduate and graduate courses, and (iii) making research outcomes broadly available through public-domain software packages and open-education resources via OpenStax courseware.The project develops novel decentralized algorithms as well as very-large scale integration (VLSI) and general-purpose computing on graphics processing units (GPGPU) architectures based on antenna clustering and parallelization, for the uplink (users communicate to base-station) and the downlink (base-station communicates to users). The main idea of decentralized baseband processing is to divide the signal-processing workload at the base-station into multiple computing fabrics that are each connected to only a subset of RF transceivers and antennas. To reduce the chip-interconnect and computation bottlenecks the project investigates (i) optimization-based algorithms that exchange consensus information among the antenna clusters and (ii) message-passing-based algorithms that avoid such consensus exchange altogether. The most promising algorithm solutions will be implemented on field-programmable gate array and GPGPU clusters to assess the efficacy and limits of the developed solutions with real-world performance, hardware, and bandwidth constraints. The results of this analysis will provide guidelines that enable optimal massive MIMO base-station designs that use decentralized baseband processing.