CIF: Small: Massive MIMO for Massive Machine-Type Communication

本项目的目标是研究大规模多输入多输出(MIMO)编码和信号处理技术，用于大规模机器类型的通信，这是新兴的5G无线系统中的一个重要场景。海量机器类型通信系统的主要特征包括海量设备连接和零星的设备活动模式。大规模MIMO技术非常适合容纳大量设备，并保持与设备数量无关的恒定能效水平。除了潜在的技术影响，研究人员计划将这项研究纳入研究生和本科生课程，并开发暑期项目，以促进K-12出游。该项目考虑由配备大量天线的基站以及大量潜在设备节点组成的大规模机器类型通信系统的上行链路，其中一部分节点在任何给定时间都处于活动状态。在发射端，为了解决设备数量大时正交导频序列的不足，将设计包含大量正交子集向量的多基码。针对节点数目较多时设备活动检测的高复杂度问题，设计了基于各种码和格拉斯曼调制星座的导频序列，其译码效率与节点数目无关。在接收端，联合设备活动检测和信道估计将利用设备活动和信道结构的稀疏性。此外，当基站使用与天线尺寸相比数量较少的射频链时，将开发矩阵或张量完成技术，用于在存在丢失数据的情况下进行信道估计或数据解码，方法是利用接收信号的低秩属性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this project is to investigate massive multiple-input multiple-output (MIMO) coding and signal processing techniques for massive machine-type communications, which is an important scenario in the emerging 5G wireless systems. Key features of the massive machine-type communication system include the massive device connectivity and the sporadic device activity pattern. The massive MIMO technique is well suited for accommodating a large number of devices and maintaining a constant level of energy efficiency that is independent of the number of devices. In addition to the potential technical impacts, the investigators plan to incorporate the research into graduate and undergraduate curricula and to also develop summer projects to facilitate K-12 outreach.This project considers the uplink of a massive machine-type communication system consisting of a base station equipped with a large number of antennas, as well as a large number of potential device nodes, out of which a fraction are active at any given time. On the transmitter side, to address the shortage of orthogonal pilot sequences when the number of devices is large, multi-base codes will be designed that contain a large number of orthogonal subsets of vectors. To address the high-complexity of device activity detection when the number of nodes is large, pilot sequences based on various codes and Grassmannian modulation constellations will be designed that can be decoded efficiently with complexity independent of the number of nodes. On the receiver side, joint device activity detection and channel estimation will be developed by exploiting the sparsity in both device activity and the channel structures. Moreover, when the base station employs a smaller number of radio frequency chains compared with the antenna size, matrix or tensor completion techniques will be developed for channel estimation or data decoding in the presence of missing data, by exploiting the low-rank property of the received signals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.