Bayesian Demodulation Algorithms for Digital Communications

数字通信的贝叶斯解调算法

• 批准号: 9406073
• 负责人: Michael Fitz
• 金额: $ 37.21万
• 依托单位: Purdue Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9406073&HistoricalAwards=false
• 关键词: Bayesian; Demodulation; Algorithms; Digital; Communications

9406073 Fitz The Bayesian filter family of algorithms is a recently developed family of deconvolution (both known and unknown channel) algorithms for digital communications. The Bayesian filters are approximations to the K-lag optimum symbol-by-symbol estimator with the unique characteristic that its resulting exponential complexity is decoupled from both the filter lag and the channel length. This project 1) will produce significant refinements to the Bayesian filter algorithms, 2) will produce an analytical characterization of these algorithms to provide greater insight into the performance, 3) will examine some applications of the algorithms in digital communications, and 4) will implement the algorithms in a fixed point DSP architecture. The Bayesian filters can be refined to include significantly more general decision feedback structures, to be applicable with more general channel models, to more accurately model time-varying fading, and to implement optimal combining algorithms. The general decision feedback structures are both similar to and a significant generalization of reduced state sequence estimation. This structure will provide an excellent tradeoff between achieving a long decoding lag and maintaining a reasonable level of complexity. The channel models considered in this project will be the autoregressive moving average model and partially observed Markov chain model. Deriving deconvolution algorithms for these channels enables a wider range of problems to be addressed by Bayesian techniques. The final generalization is in the modeling of time-varying channels. If high performance deconvolution can be accomplished in a rapidly fading frequency selective channel, then significant improvements are available in the capacity of digital communication systems (especially ones using TDMA). Finally, methods for optimally combining more than one observation per symbol will be considered in a Bayesian framework. Typical applications of this type include antenna diversity systems and fractional spaced equalization. The Bayesian filter has a great many potential applications areas and these applications will be explored in detail. The areas targeted are: reduced complexity demodulation of nonlinear modulations with memory; reduced complexity decoding of error control codes; high speed high frequency (HF) digital communications; magnetic recording; and land mobile radio. ***

小行星9406073 贝叶斯滤波算法族是最近开发的用于数字通信的解卷积（已知和未知信道）算法族。 贝叶斯滤波器是K-滞后最优逐符号估计器的近似，其独特的特性是其所得的指数复杂度与滤波器滞后和信道长度解耦。 该项目1）将产生显着的改进贝叶斯滤波器算法，2）将产生这些算法的分析特性，以提供更深入的了解性能，3）将检查在数字通信中的算法的一些应用程序，和4）将实现在一个定点DSP架构的算法。 贝叶斯过滤器可以被细化 包括明显更一般的判决反馈结构、可应用于更一般的信道模型、更精确地对时变衰落建模、以及实现最佳组合算法。 一般的判决反馈结构既类似于约简状态序列估计，又是约简状态序列估计的重要推广。 该结构将在实现长解码延迟和保持合理的复杂度水平之间提供极好的折衷。 本计画所考虑的通道模式将是自回归移动平均模式与部分可观察马尔可夫链模式。 推导出这些通道的反卷积算法，使更广泛的问题得到解决的贝叶斯技术。 最后的概括是时变信道的建模。 如果在快速衰落的频率选择性信道中可以实现高性能的反卷积，则在 数字通信系统（特别是使用TDMA的系统）的容量。 最后，将在贝叶斯框架中考虑每个符号最佳组合多个观测的方法。 这种类型的典型应用包括天线分集系统和分数间隔均衡。 贝叶斯滤波器有很多潜在的应用领域，这些应用将被详细探讨。 所针对的领域是：具有存储器的非线性调制的降低复杂度的解调;差错控制码的降低复杂度的解码;高速高频（HF）数字通信;磁记录;以及陆地移动的无线电。 ***