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

对无线服务和更高的无线吞吐量的需求继续呈指数级增长。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使能技术。实现这一愿景的一个挑战是随着发射天线的数量变得越来越大，对发射机和接收机之间的无线信道进行估计。本研究针对频分双工(FDD)系统、时分双工(TDD)系统和分布式大规模MIMO系统等各种部署场景，研究了信道建模和估计所面临的挑战。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，他们将接受最新无线技术的培训，并产生具有基础和更广泛重要性的新工具。信道建模和估计研究包括通过稀疏贝叶斯学习等高级稀疏信号恢复算法开发视距信道估计，目标是减少训练开销。从新的词典学习角度考虑非视线环境，以实现频道的低维表示。这些表示与压缩信道学习一起将导致显著降低FDD系统的反馈开销的技术的发展。对于时分双工系统，研究涉及数据辅助的信道估计技术的发展，以改善信道估计，远远超过仅使用导频训练的可能。此外，该研究还包括对分布式大规模MIMO阵列设计的权衡进行深入研究，以获得选择最佳阵列配置所必需的见解。

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.