Collaborative Research: BAMM: Baseband Accelerators for Massive Multiple-Input Multiple-Output (MIMO) Technology

合作研究：BAMM：大规模多输入多输出 (MIMO) 技术的基带加速器

• 批准号: 1408370
• 负责人: Joseph Cavallaro
• 金额: $ 16.57万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408370&HistoricalAwards=false
• 关键词: Collaborative; Research; BAMM; Baseband; Accelerators

Wireless communication is one of the fastest growing technologies worldwide creating ubiquitous access to mobile broadband services. The transition from one to several (typically two to four) antennas at both ends of the wireless link, known as multiple-input multiple-output (MIMO), was a key enabler for the growing data rates during the last decade. However, as the demands for data rates on the network are expected to increase by more than 1000x by 2020, novel transmission technologies beyond small-scale MIMO become necessary. This project will leverage recent theoretical results in massive MIMO, which promise that the use of hundreds of antennas at the base-station will enable orders-of-magnitude higher data rates than conventional small-scale MIMO systems. Since existing algorithms and current integrated circuit architectures are unable to sustain the excessive complexity caused by the massive amount of received data streams, this project will jointly consider algorithms and efficient (in terms of cost and power) computing architectures. The project will analyze system trade-offs to develop low-complexity algorithms and corresponding integrated circuits that will enable the capabilities of massive MIMO. Another goal of this project in terms of broader impact is to develop open-access education materials and semester-length courses on algorithms and hardware design aspects of massive MIMO. The computational complexity of existing data detection, multi-user interference suppression (pre-coding), and impairment-compensation algorithms in systems relying on massive MIMO grows super-linear in the number of base-station antennas. Thus, the associated computational complexity prevents the use of these algorithms in wireless systems having hundreds of antennas. To enable massive MIMO in future systems, the project develops a set of novel computationally efficient algorithms for detection, pre-coding, and impairment compensation that can be implemented in dedicated digital very-large scale integration (VLSI) circuits at low complexity and power. The proposed methods will rely on approximate algorithms using series expansions and convex optimization, which approaches optimal performance as the number of base-station antennas increases. Furthermore, novel antenna-selection schemes are developed with the goal of reducing the complexity and hardware costs incurred by the presence of hundreds of base-station antennas. In addition to theoretical analyses and algorithm development, experimental evaluation of the developed baseband accelerators will be conducted on early-stage academic prototype platforms. The results of this evaluation will be used to assess the performance, complexity, and power consumption of the developed algorithm accelerator designs in realistic environments and to identify both the potential capabilities and limits of massive MIMO systems.

无线通信是全球发展最快的技术之一，创造了无处不在的移动宽带服务。在无线链路的两端从一个天线到几个（通常是两个到四个）天线的过渡，称为多输入多输出（MIMO），是过去十年中数据速率不断增长的关键促成因素。然而，由于到2020年对网络数据速率的需求预计将增加1000倍以上，因此需要超越小规模MIMO的新型传输技术。该项目将利用大规模MIMO的最新理论成果，该成果承诺在基站中使用数百个天线将使数据速率比传统的小规模MIMO系统高几个数量级。由于现有的算法和现有的集成电路架构无法承受大量接收数据流造成的过度复杂性，因此本项目将共同考虑算法和高效（在成本和功耗方面）的计算架构。该项目将分析系统权衡，以开发低复杂性算法和相应的集成电路，从而实现大规模MIMO的能力。就更广泛的影响而言，该项目的另一个目标是开发关于大规模MIMO算法和硬件设计方面的开放获取教育材料和学期课程。在依赖大规模MIMO的系统中，现有的数据检测、多用户干扰抑制（预编码）和损伤补偿算法的计算复杂度随着基站天线数量的增加呈超线性增长。因此，相关的计算复杂性阻碍了这些算法在具有数百个天线的无线系统中的使用。为了在未来的系统中实现大规模MIMO，该项目开发了一套新的计算效率高的算法，用于检测、预编码和损伤补偿，这些算法可以在专用的数字超大规模集成（VLSI）电路中以低复杂性和低功耗实现。所提出的方法将依赖于使用序列展开和凸优化的近似算法，该算法随着基站天线数量的增加而接近最佳性能。此外，还开发了新的天线选择方案，以降低由于存在数百个基站天线而导致的复杂性和硬件成本。除了理论分析和算法开发之外，所开发的基带加速器还将在早期的学术原型平台上进行实验评估。该评估结果将用于评估在现实环境中开发的算法加速器设计的性能、复杂性和功耗，并确定大规模MIMO系统的潜在能力和限制。