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: 1408006
• 负责人: Christoph Studer
• 金额: $ 16.57万
• 依托单位: Cornell 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 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408006&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.

无线通信是全世界发展最快的技术之一，其创建了对移动的宽带服务的无处不在的接入。无线链路两端的天线从一个过渡到几个（通常是两到四个），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推动因素。然而，随着对网络数据速率的需求预计到2020年将增加1000倍以上，小规模MIMO之外的新型传输技术变得必要。该项目将利用大规模MIMO的最新理论成果，这些成果承诺在基站使用数百个天线将使数据速率比传统的小规模MIMO系统高出几个数量级。由于现有的算法和当前的集成电路架构无法承受接收到的大量数据流所造成的过度复杂性，因此该项目将联合考虑算法和高效（在成本和功耗方面）的计算架构。该项目将分析系统权衡，以开发低复杂度算法和相应的集成电路，从而实现大规模MIMO的功能。该项目在更广泛影响方面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。在依赖于大规模MIMO的系统中，现有数据检测、多用户干扰抑制（预编码）和损伤补偿算法的计算复杂度在基站天线的数量上超线性地增长。因此，相关联的计算复杂性阻止了在具有数百个天线的无线系统中使用这些算法。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数字超大规模集成电路（VLSI）中以低复杂度和低功耗实现。所提出的方法将依赖于使用级数展开和凸优化的近似算法，其随着基站天线数量的增加而接近最佳性能。此外，新的天线选择方案的目标是减少的复杂性和硬件成本所产生的数百个基站天线的存在。除了理论分析和算法开发外，还将在早期学术原型平台上对所开发的基带加速器进行实验评估。该评估的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。