基于贝叶斯模型的长码DS-CDMA信号扩频码与信息序列联合估计

张立民; 钟兆根; 武恒州

doi:10.7527/S1000-6893.2013.0205

航空学报 >

2013 , Vol. 34 >Issue 5: 1191 - 1203

DOI: https://doi.org/10.7527/S1000-6893.2013.0205

电子与控制

基于贝叶斯模型的长码DS-CDMA信号扩频码与信息序列联合估计

张立民 ,
钟兆根 ,
武恒州

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1. 海军航空工程学院电子信息工程系, 山东烟台 264001;
2. 海军驻沈阳地区航空军事代表室, 辽宁沈阳 110034

张立民男, 博士, 教授, 博士生导师。主要研究方向: 卫星信号处理、武器系统仿真等。 Tel: 0535-6635402 E-mail: iamzlm@163.com;钟兆根男, 博士研究生。主要研究方向: 通信侦察和空间信息对抗。 Tel: 0535-6635887 E-mail: zhongzhaogen@163.com;武恒州男, 硕士, 工程师。主要研究方向: 航空武器装备仿真。 Tel: 010-66950748 E-mail: wuhengzhou2005@163.com

收稿日期: 2012-06-04

修回日期: 2012-10-10

网络出版日期: 2012-10-25

基金资助

国家自然科学基金(60972159,61102167);航空科学基金(20085184003)

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Joint Estimation of Spreading Codes and Information Sequences for Long Code DS-CDMA Signals Based on Bayesian Model

ZHANG Limin ,
ZHONG Zhaogen ,
WU Hengzhou

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1. Department of Electronics and Information Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China;
2. Naval Aviation Military Representative Office in Shenyang, Shenyang 110034, China

Received date: 2012-06-04

Revised date: 2012-10-10

Online published: 2012-10-25

Supported by

National Natural Science Foundation of China (60972159,61102167); Aeronautical Science Foundation of China (20085184003)

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摘要

针对长码直接序列扩频码分多址(DS-CDMA)信号的盲解扩,在信号模型分析的基础上,提出了一种基于可逆跳跃的马尔可夫链蒙特卡罗(RJ-MCMC)扩频码和信息序列联合估计算法。该算法分别对建立的联合后验分布模型进行迭代抽样,并有效地在不同维数的子空间中跳转,从而构造一条马尔可夫链,使其平稳分布为待估参数的后验分布,最终拼接得到每个用户的完整扩频序列和信息序列估计。仿真结果表明:该方法在迭代二十几步时达到收敛;并且在功率相同和不同条件下,当信噪比(SNR)大于-9 dB时,估计序列与真实序列的相似度均超过0.95,信息序列的误码率低于0.01;同时算法对不同用户个数和不同调制样式具有较强的适应性,与Fast-ICA算法相比,估计性能平均提高了约3 dB。

关键词： 扩频通信; 码分多址; 贝叶斯模型; 吉布斯采样; 马尔可夫链蒙特卡罗; 可逆跳跃

本文引用格式

张立民 , 钟兆根 , 武恒州 . 基于贝叶斯模型的长码DS-CDMA信号扩频码与信息序列联合估计[J]. 航空学报, 2013 , 34(5) : 1191 -1203 . DOI: 10.7527/S1000-6893.2013.0205

Abstract

To deal with the blind dispreading of long code direct-sequence code division multiple access (DS-CDMA) signals, this paper introduces an algorithm for joint spreading codes and information sequences estimation based on reversible jump Markov chain Monte Carlo (RJ-MCMC) by analyzing a signal model. The proposed algorithm analyzes and processes the signal models separately, and obtains the samples of distribution to be estimated through iterative sampling. It is able to construct a reversible Markov chain sampler that jumps between parameter subspaces of different dimensionality, so that the posterior distribution of parameters to be estimated is obtained. Finally, it estimates the entire spreading code and information sequence of each user by splicing. The simulation shows that the iteration converges after some twenty steps. Regardless of whether the power is equal or unequal, when the signal-to-noise ratio (SNR) is greater than -9 dB, the similarity degree between the estimated sequence and true sequence exceeds 0.95, with the bit error rate of information sequence less than 0.01. In addition, the algorithm has good adaptability for different number of users and different modulation styles. Compared with the fast-ICA algorithm, this algorithm improves the estimation performance by an average of about 3 dB.

Key words： spread spectrum communication; code division multiple access; Bayesian model; Gibbs sampler; Markov chain Monte Carlo; reversible jump

参考文献

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