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.

对无线服务和较高无线吞吐量的需求继续呈指数增长。为了实现这一增长，在下一代无线系统中，大量的多输入多输出（MIMO）被确定为一种启示技术。实现视觉的挑战是随着发射天线的数量变大，发射器和接收器之间的无线通道的估计。在本研究中针对各种部署方案提出了渠道建模和估计挑战。频级双链体（FDD）系统，时间分级双工（TDD）系统和分布式大型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外，这项研究还将涉及几名研究生，这些研究生将接受最新无线技术的培训，还导致具有基本和更广泛进口的新颖工具。渠道建模和估计研究的渠道模型和估计研究包括通过较高的稀疏信号恢复的跨度跨室估算的开发，包括Sparse smights Algorith over troge Inthecien of Sparse Algorith与RED一起培训。从新颖的词典学习角度考虑了非视线环境，以实现该频道的低维表示。这些表示以及压缩渠道学习将导致技术的发展，从而大大减少FDD系统的反馈开销。对于TDD系统，该研究涉及数据辅助渠道估计技术的开发，以改善频道估计值，远远超出了仅通过试验训练的可能性。此外，该研究还包括对分布式大型MIMO阵列设计的权衡的深入研究，以开发选择最佳阵列配置所需的见解。