Collaborative: Quantizing Wireless Channels

移动无线通信正在成为互联网接入的推动者。不幸的是，由于与无线通信相关的基本问题：带宽稀缺和质量差，移动网络无法提供互联网用户所期望的高速接入。为了克服这些问题，本研究开发了一系列算法，使发射机能够响应传播信道的变化。这项工作的主要创新是开发了压缩传播信道信息的新方法。这个信息从接收机发回发射机，帮助发射机调整发射的信号。本研究的方法是开发一个新的源编码框架：无线信道。这与传统的源编码的不同之处在于，目标是提高通信理论系统的性能，而不是提高重建的保真度。研究的目标是确定哪些信道状态信息应该量化；开发量化信道基本参数的算法；推导出量化保真度的合适通信理论概念，如互信息和误码率；描述反馈率和网络性能之间的权衡；并面对实际问题所带来的估计误差，误差和延迟的反馈通道，以及实现的约束。预计这项工作将在不同领域产生更广泛的影响，包括以新算法、理论结果和见解的形式进行研究；行业通过行业合作伙伴开发的移动网络应用程序；通过训练有素的工程师进行教育，为少数族裔学生提供研究经验，提供指导，并通过公开的课件在全球范围内加强学习。

Mobile wireless communication is becoming the enabler of Internet access. Unfortunately, mobile networks are not equipped to provide the high-speed access that Internet users expect due to fundamental problems associated with wireless communication: scarce bandwidth and poor quality. To overcome these problems, this research develops a family of algorithms that allow the transmitter to respond to changes in the propagation channel. The major innovation in this work is the development of new methods for compressing information about the propagation channel. This information is sent from the receiver back to the transmitter to help the transmitter adjust the transmitted signal. The approach to this research is to develop a framework for source coding with a new source: the wireless channel. This differs from traditional source coding in that the objective is to improve communication theoretic system performance as opposed to improving the fidelity of the reconstruction. The objectives of the research are to determine what channel state information should be quantized; develop algorithms for quantizing the essential parameters of the channel; derive suitable communication theoretic notions of fidelity of the quantization such as mutual information and bit error rate; characterize the tradeoff between feedback rate and network performance; and confront practical issues introduced by estimation error, errors and delay in the feedback channel, and implementation constraints.The broader impacts of the work are expected in diverse areas including research in the form of new algorithms, theoretical results, and insights; industry through mobile network applications developed by industry partners; and education through better trained engineers, research experiences for minority students, mentoring, and enhanced learning worldwide thanks to publicly available courseware.