Adaptive recursive least-squares maximum-likelihood sequence estimation with higher-order state variable model of radio channels—adaptive performance improvement of rls-mlse
Adaptive recursive least-squares maximum-likelihood sequence estimation with higher-order state variable model of radio channels—adaptive performance improvement of rls-mlse
复制标题
无线信道高阶状态变量模型的自适应递归最小二乘最大似然序列估计——rls-mlse的自适应性能改进
DOI：
10.1002/ecja.4410760907
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
H. Suzuki
中科院分区：
文献类型：
--
作者：
K. Fukawa;H. Suzuki
通过对最大似然信号估计理论中的自适应递归最小二乘最大似然序列估计（RLS-MLSE）的信道模型进行扩展，获得了对快衰落移动的无线信道的上级跟踪性能。传统的自适应最大似然序列估计器使用简单的马尔可夫模型或随机游走模型来描述频率选择性衰落信道的脉冲响应的波动。
在本文中，二阶马尔可夫模型，采用了一阶时间导数的脉冲响应。这种高阶状态变量的方法提高了信道估计算法的适应性，并给出了信号传输性能的显着改善。计算机仿真表明，基于该高阶状态变量模型的RLS-MLSE在40 kbit/s QPSK下具有良好的性能，最大多普勒频率可达160 Hz。
Superior tracking performance for fast fading mobile radio channels is obtained by extending channel models used in the adaptive recursive least squares maximum likelihood sequence estimation (RLS-MLSE) derived from the theory of maximum likelihood signal estimation. Conventional adaptive maximum likelihood sequence estimators used the simple Markov model or random walk model to describe fluctuations of the impulse response of a frequency-selective fading channel.
In this paper, a second-order Markov model that incorporates the first-order time derivative of the impulse response is used. This higher-order state variable approach improves the adaptability of the channel estimation algorithm and gives a significant improvement in signal transmission performance. Computer simulations show that the RLS-MLSE based on this higher-order state variable model yields good performance of 40 kbit/s QPSK with the maximum Doppler frequency up to 160 Hz.