ITR/SY + SI (C-CR) Collaborative Research: Signal Processing and Channel Modelling in Low-Dimensional Subspaces for Wireless Communication in Multi-Antenna Systems

题目：多天线系统无线通信低维子空间信号处理与信道建模摘要固定基站与众多移动外设之间的信息通信问题是当前无线通信研究的主要问题。这个问题在技术上具有挑战性，因为连接基站和外设的信道是随机和时变的。最通用的解决方案需要与信道“匹配”的空时编码和信号处理。本研究计划的目标是将空时阵列处理（STAP）理论扩展到衰落信道上的多天线无线通信，以确定何时以及如何利用信道结构进行空时信号处理增益和/或分集。从理论上讲，本研究的结果促进了对天线阵列间多天线无线通信的认识。实际上，他们提出了在固定基站和移动设备之间以及固定计算机和移动存储区域网络之间进行自适应通信的方法。主要研究人员先前的结果表明，优化发射分集的信号设计只需要低维信道子空间的跨度，而不需要其坐标表示的详细物理特性，以及优化时空处理增益和分集之间权衡的收发器。此外，最优的收发统计量只是输出信噪比（SINR）的估计量，这表明解码数据的收发统计量可以用来估计和跟踪输出SINR和网络容量。这意味着功率和带宽可以自适应地分配，仅使用收发器统计信息。因此，总的来说，这个程序是针对时空信道建模，使用子空间方法；子空间信号和收发器增益和分集设计；并且估计网络容量时，只使用收发器的统计数据。

ITR Small Proposal 0112508/ 0112573Friedlander/ScharfTitle: Signal Processing and Channel Modelling in Low-DimensionalSubspaces for Wireless Communication in Multi-Antenna SystemsAbstractThe problem of communicating information between a fixed basestation and many mobile peripherals dominates much of current research in wireless communication. The problem is tecnologically challenging because the channel that connects the basestation to the peripherals is random and time-varying. The most general solution requires space-time coding and signal processing that is "matched" to the channel. The objective of this research program is to extend the theory of space-time array processing (STAP) developed for radar applications, to multi-antenna wireless communication over fading channnels, in order to determine whenand how channel structure can be exploited for space-time signal processing gain and/or diversity. Theoretically, the results of this research advance the understanding of multi-antenna wireless communication between antenna arrays. Practically, they suggest methods for adaptively communicating between fixed basestations and mobiles and between fixed computers and movable storage area networks.Prior results by the principal investigators suggest that only the span of a low-dimensional channel subspace, and not the detailed physics of its coordinate representation, is required for the design of signals that optimize transmit diversity, and tranceivers that optimize the trade off between space-time processing gain and diversity. Moreover, the optimum transceiver statistics are just estimators of output signal-to-interference plus noise ratio (SINR), suggesting that transceiver statistics for decoding data can be used to estimate and track output SINR and network capacity. This means power and bandwidth may be adaptively allocated, using only transceiver statistics. So, in broad outline, this program is addressed to space-time channel modelling, using subspace methods; subspace signal and transceiver design for gain and diversity; and estimation of network capacity, using only transceiver statistics.