Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering

Rong Chen, Xiaodong Wang, Jun S. Liu
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引用次数: 223

Abstract

A novel adaptive Bayesian receiver for signal detection in flat-fading channels is developed based on the sequential Monte Carlo methodology. The basic idea is to treat the transmitted signals as missing data and to sequentially impute multiple copies of them based on the observed signals. The imputed signal sequences, together with their importance weights, provide a way to approximate the Bayesian estimate of the transmitted signals and the channel states. It is shown through simulations that the proposed sequential Monte Carlo receivers achieve near-bound performance in fading channels without the aid of any training/pilot symbols or decision feedback. Moreover, the proposed receiver structure exhibits massive parallelism and is ideally suited for high-speed parallel implementation using the VLSI systolic array technology.
基于混合卡尔曼滤波的平衰落信道自适应联合检测与解码
基于序贯蒙特卡罗方法,提出了一种用于平衰落信道信号检测的自适应贝叶斯接收机。其基本思想是将传输的信号视为缺失数据,并根据观测到的信号依次进行多个拷贝。输入的信号序列及其重要权重提供了一种近似传输信号和信道状态的贝叶斯估计的方法。仿真结果表明,所提出的时序蒙特卡罗接收机在不借助任何训练/导频符号或决策反馈的情况下,在衰落信道中获得了近界性能。此外,所提出的接收器结构具有大规模并行性,非常适合使用VLSI收缩阵列技术实现高速并行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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