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

ITR小提案0112508/0112573 Friedlander/Scharf标题：多天线系统中无线通信的低维子空间中的信号处理和信道建模摘要在固定基站和许多移动外围设备之间进行信息通信的问题主导了当前无线通信的许多研究。这个问题在技术上具有挑战性，因为连接基站和外围设备的通道是随机和时变的。最普遍的解决方案需要与信道“匹配”的空时编码和信号处理。该研究计划的目的是将空时阵列处理(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来获得空时信号处理增益和/或分集。从理论上讲，本文的研究成果加深了对天线阵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台之间以及固定计算机和移动存储区域网络之间自适应通信的方法。主要研究人员之前的结果表明，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原理，就可以设计优化发射分集的信号，以及优化时空处理增益和分集之间权衡的收发信机。此外，最佳收发信机统计量只是输出信干噪比(SINR)的估计器，这表明解码数据的收发信机统计量可用于估计和跟踪输出SINR和网络容量。这意味着可以仅使用收发机统计数据自适应地分配功率和带宽。因此，概括地讲，本程序涉及使用子空间方法的空时信道建模；子空间信号和收发机的增益和分集设计；以及仅使用收发机统计数据的网络容量估计。

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 when and 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.