Collaborative Research: Quantizing Wireless Channels

移动无线通信正在成为互联网接入的使能器。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期望的高速访问。为了克服这些问题，这项研究开发了一系列算法，使发射机能够响应传播通道的变化。这项工作的主要创新是开发了压缩关于传播通道的信息的新方法。该信息从接收器发送回发射器，以帮助发射器调整发射的信号。这项研究的方法是开发一个框架，使用一个新的信源：无线信道进行信源编码。这与传统信源编码的不同之处在于，其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。研究的目标是确定应该量化什么信道状态信息；开发量化信道的基本参数的算法；推导量化保真度的合适的通信理论概念，例如互信息和误码率；描述反馈速率和网络性能之间的权衡；以及面临由估计误差、反馈信道中的误差和延迟以及实施限制引入的实际问题。这项工作的更广泛的影响预计将在不同的领域产生，包括以新算法、理论结果和见解的形式进行的研究；行业合作伙伴开发的移动网络应用；并通过更好地培训工程师、为少数族裔学生提供研究经验、指导和加强全球范围内的学习来进行教育，这要归功于公开可用的课件。

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.