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

NeTS：小型：协作研究：BRICK：突破大规模 MIMO 基站的 I/O 和计算瓶颈

• 批准号: 1717218
• 负责人: Joseph Cavallaro
• 金额: $ 25万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717218&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; BRICK

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.

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，与现有蜂窝通信系统相比，将实现前所未有的数据速率、小区覆盖范围和传输可靠性。然而，数百或数千个射频 (RF) 收发器和天线的存在会导致内部数据速率过高，从而导致芯片互连和计算瓶颈，从而阻碍该技术在实践中的直接部署。该项目将开发依赖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，使基站设计能够具有数千个天线元件，而无需牺牲性能或可靠性。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、Argos 大规模 MIMO 测试台和其他网络研究测试台的合作来推进未来的蜂窝网络。该项目对教育和推广的更广泛影响将包括多个组成部分，包括（i）培训多元化的学生群体，作为通信理论和电路设计领域协作、多机构研究团队的一部分，（ii）将工作成果整合到本科生和研究生课程中，以及（iii）通过公共领域软件包和 OpenStax 课件的开放教育资源广泛提供研究成果。 上行链路（用户与基站通信）和下行链路（基站与用户通信）基于天线集群和并行化的去中心化算法以及超大规模集成（VLSI）和图形处理单元（GPGPU）架构上的通用计算。分散式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子集。为了减少芯片互连和计算瓶颈，该项目研究了（i）在天线簇之间交换共识信息的基于优化的算法，以及（ii）完全避免这种共识交换的基于消息传递的算法。最有前途的算法解决方案将在现场可编程门阵列和 GPGPU 集群上实施，以评估所开发解决方案在实际性能、硬件和带宽限制下的功效和局限性。该分析的结果将为实现使用分散基带处理的最佳大规模 MIMO 基站设计提供指导。

项目成果

Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

大规模 MU-MIMO 系统中分散式基带处理的设计权衡

• DOI: 10.1109/ieeeconf44664.2019.9048727
• 发表时间: 2019
• 期刊: Systems and Computers
• 作者: Li, Kaipeng;McNaney, James;Tarver, Chance;Castaneda, Oscar;Jeon, Charles;Cavallaro, Joseph R.;Studer, Christoph
• 通讯作者: Studer, Christoph

Decentralized Equalization With Feedforward Architectures for Massive MU-MIMO

• DOI: 10.1109/tsp.2019.2928947
• 发表时间: 2018-08
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Charles Jeon;Kaipeng Li;Joseph R. Cavallaro;Christoph Studer

OFDM-Based Beam-Oriented Digital Predistortion for Massive MIMO

用于大规模 MIMO 的基于 OFDM 的波束定向数字预失真

• DOI: 10.1109/iscas51556.2021.9401479
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Circuits and Systems (ISCAS
• 作者: Tarver, Chance;Balatsoukas-Stimming, Alexios;Studer, Christoph;Cavallaro, Joseph R.
• 通讯作者: Cavallaro, Joseph R.

GPU-Based LDPC Decoding for vRAN Systems in 5G and Beyond

适用于 5G 及更高版本 vRAN 系统的基于 GPU 的 LDPC 解码

• DOI: 10.1109/iscas45731.2020.9181064
• 发表时间: 2020
• 期刊: 2020 IEEE International Symposium on Circuits and Systems (ISCAS
• 作者: Tarver, Chance;Tonnemacher, Matthew;Chen, Hao;Zhang, Jianzhong Charlie;Cavallaro, Joseph R.
• 通讯作者: Cavallaro, Joseph R.

Decentralized equalization for massive MU-MIMO on FPGA

• DOI: 10.1109/acssc.2017.8335613
• 发表时间: 2017-10
• 期刊: 2017 51st Asilomar Conference on Signals, Systems, and Computers
• 作者: Kaipeng Li;Charles Jeon;Joseph R. Cavallaro;Christoph Studer