Adaptive Bayesian Receivers in Fading Channels: A Sequential Monte Carlo Filtering Design Paradigm

衰落通道中的自适应贝叶斯接收器：顺序蒙特卡罗滤波设计范式

• 批准号: 0225826
• 负责人: Xiaodong Wang
• 金额: $ 29.11万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2004-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0225826&HistoricalAwards=false
• 关键词: Adaptive; Bayesian; Receivers; Fading; Channels

AbstractThe coming generation of tetherless communication technology promises a giant leap forward in informationaccessibility. Advanced features of the so-called fourth-generation wireless systems and beyond, such as data rates compatible with multimedia applications, will enable many emerging applications not possible with current wireless systems. However, it is not at all clear how wireless receivers should be optimally designed to meet the technical challenges introduced by the wider bandwidths and higher data rates inherent in the future wireless systems. It is generally believed that the real niche for future wireless receivers lies in the development of adaptive systems to perform sophisticated signal processing functions. But, at this time there is a lack of concrete principles that can be used to design these futuristic receivers. It is important at this stage to acquire the insights and theoretical tools that may help spark revolutionary breakthroughs in this field.Investigation of design methodologies of adaptive Bayesian receivers in single-user and multiuser fading channels is proposed. The approach is to formulate the problems of signal reception in unknown time-varying channels as multivariate Bayesian inference problems. Sequential Monte Carlo filtering methods, the relatively simple but extremely powerful numerical techniques recently developed in the field of statistics, will be employed to develop adaptive systems for computing the Bayesian estimates of the channels and data. An array of receiver design problems found in wireless communications, such as mitigation of various types of radio-frequency interference (including multiple-access interference, narrowband interference, impulsive noise), tracking of fading channels, resolving multipath channel dispersion, space-time processing by multiple antennas, exploiting coded signal structures, etc., will be treated under the unified framework of sequential Monte Carlo Bayesian estimation. The theoretical effort in this project is expected to culminate in the formulation of novel receiver design concepts applicable for future wireless systems.

下一代无线通信技术有望在信息可访问性方面实现巨大飞跃。所谓的第四代无线系统及以后的先进特征，例如与多媒体应用兼容的数据速率，将使当前无线系统不可能实现的许多新兴应用成为可能。然而，根本不清楚无线接收器应该如何被最佳地设计以满足由未来无线系统中固有的更宽带宽和更高数据速率引入的技术挑战。一般认为，未来无线接收机的真实的利基在于开发执行复杂信号处理功能的自适应系统。但是，在这个时候，缺乏具体的原则，可以用来设计这些未来的接收器。重要的是在这个阶段，以获得的见解和理论工具，可能有助于引发革命性的突破，在这一领域。调查的设计方法，自适应贝叶斯接收机在单用户和多用户衰落信道提出。该方法是制定在未知的时变信道中的信号接收的问题作为多元贝叶斯推理问题。序贯蒙特卡罗滤波方法，相对简单，但非常强大的数值技术最近在统计领域的发展，将开发自适应系统计算贝叶斯估计的通道和数据。在无线通信中发现的一系列接收机设计问题，诸如减轻各种类型的射频干扰（包括多址干扰、窄带干扰、脉冲噪声）、跟踪衰落信道、解决多径信道色散、通过多个天线进行空时处理、利用编码信号结构等，将在序贯蒙特卡罗贝叶斯估计的统一框架下进行处理。在这个项目中的理论努力，预计最终在制定新的接收机设计概念适用于未来的无线系统。