CIF: SMALL: MASSIVE MIMO SYSTEMS: Novel Channel Modeling and Estimation Methods

CIF：小型：大规模 MIMO 系统：新颖的信道建模和估计方法

• 批准号: 1617365
• 负责人: Bhaskar Rao
• 金额: $ 30万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617365&HistoricalAwards=false
• 关键词: CIF; SMALL; MASSIVE; MIMO; SYSTEMS

The demand for wireless services and higher wireless throughput continues to grow exponentially. To meet this growth, massive multiple-input multiple-output (MIMO) has been identified as an enabling technology in next generation wireless systems. A challenge in realizing the vision is the estimation of the wireless channel between the transmitter and receiver as the number of transmitting antennas becomes large. The channel modeling and estimation challenge is addressed in this research for a variety of deployment scenarios; frequency division duplex (FDD) systems, time division duplex (TDD) systems, and distributed massive MIMO systems. In addition to having a significant impact on the theoretical foundations and algorithms relevant to next generation wireless systems, this research will involve several graduate students who will be trained in the latest wireless technology and also result in novel tools that have fundamental and wider import.The channel modeling and estimation research includes the development of line-of-sight channel estimation via advanced sparse signal recovery algorithms like sparse Bayesian learning with the goal of reducing training overhead. The non-line-of-sight environment is considered from a novel dictionary learning perspective to enable low dimensional representations of the channel. These representations along with compressive channel learning will lead to the development of techniques that significantly reduce the feedback overhead for FDD systems. For TDD systems, the research involves the development of data-aided channel estimation techniques to improve channel estimates well beyond what is possible with pilot-only training. In addition, the research includes an in-depth study of the tradeoffs of distributed massive MIMO array design to develop insights necessary for selecting the optimal array configuration.

对无线服务和更高无线吞吐量的需求持续呈指数增长。为了满足这种增长，大规模多输入多输出 (MIMO) 已被确定为下一代无线系统的支持技术。实现这一愿景的一个挑战是随着发射天线数量的增加，对发射器和接收器之间的无线信道进行估计。本研究针对各种部署场景解决了信道建模和估计挑战；频分双工 (FDD) 系统、时分双工 (TDD) 系统和分布式大规模 MIMO 系统。除了对与下一代无线系统相关的理论基础和算法产生重大影响之外，这项研究还将涉及几名研究生，他们将接受最新无线技术的培训，并产生具有基础性和更广泛意义的新颖工具。信道建模和估计研究包括通过稀疏贝叶斯学习等先进的稀疏信号恢复算法开发视距信道估计，以减少训练开销。从新颖的字典学习角度考虑非视距环境，以实现通道的低维表示。这些表示以及压缩信道学习将导致显着降低 FDD 系统反馈开销的技术的发展。对于 TDD 系统，研究涉及数据辅助信道估计技术的开发，以改进信道估计，远远超出仅导频训练所能达到的水平。此外，该研究还深入研究了分布式大规模 MIMO 阵列设计的权衡，以得出选择最佳阵列配置所需的见解。