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

ITR小型提案0112508/0112573 Friedlander/Scharf题目：多天线系统中用于无线通信的低信噪比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的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。这个问题在技术上是具有挑战性的，因为连接基站和外围设备的信道是随机的和时变的。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。本研究计划的目的是扩展的空时阵列处理（STAP）理论的雷达应用，多天线无线通信衰落信道，以确定何时以及如何信道结构可以利用空时信号处理增益和/或多样性。理论上，本文的研究成果推进了对天线阵列间多天线无线通信的理解。实际上，他们提出了固定基站和移动站之间以及固定计算机和可移动存储区域网络之间自适应通信的方法。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，以及优化空时处理增益和分集之间的折衷的收发器。此外，最佳收发器统计量只是输出信号与干扰加噪声比（SINR）的估计量，这表明用于解码数据的收发器统计量可以用于估计和跟踪输出SINR和网络容量。这意味着功率和带宽可以仅使用收发器统计来自适应地分配。因此，在广泛的轮廓，该计划是针对空时信道建模，使用子空间方法;子空间信号和收发器的增益和分集设计;和网络容量的估计，仅使用收发器统计。

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 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.