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

对无线服务和更高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）已经被确定为下一代无线系统中的使能技术。实现这一愿景的一个挑战是，随着发射天线数量的增加，发射机和接收机之间的无线信道的估计。信道建模和估计的挑战是解决在本研究中的各种部署方案，频分双工（FDD）系统，时分双工（TDD）系统，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具有重大影响之外，这项研究将涉及几名研究生，他们将接受最新无线技术的培训，并将产生具有基础性和更广泛意义的新工具。通过高级稀疏信号恢复算法（如稀疏贝叶斯学习）进行视线信道估计，目标是减少训练开销。非视线环境被认为是从一个新的字典学习的角度，使低维表示的通道。这些表示沿着压缩信道学习将导致显著减少FDD系统的反馈开销的技术的发展。对于TDD系统，该研究涉及数据辅助信道估计技术的发展，以改善信道估计远远超过可能与导频的唯一训练。此外，该研究还包括深入研究分布式大规模MIMO阵列设计的权衡，以发展选择最佳阵列配置所需的见解。

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.