基于RJ-MCMC的DS-CDMA信号扩频码与信息序列盲估计

针对非合作低信噪比环境下的DS-CDMA信号参数估计问题，在分析信号模型的基础上，提出一种基于可逆跳跃的马尔科夫链蒙特卡罗(RJ-MCMC)扩频序列和信息序列联合估计算法。该算法通过建立信号参数和用户个数的联合后验分布模型，迭代抽样得到待估分布的样本，并有效地在不同维数的子空间中跳转，从而构造一条马尔科夫链，使其平稳分布为待估参数的后验分布。仿真结果表明，该算法在功率相同和不同的条件下均能适应较低的信噪比，并且对不同的用户个数具有较强的适应性，同时算法的估计性能相对现有的方法也有很大的改善。

Blind Estimation of Spreading Codes and Information Sequences for DS-CDMA Signal Based on RJ-MCMC

An algorithm for joint spreading sequence and information sequence estimation based on reversible jump Markov chain Monte Carlo (RJ-MCMC) for direct-sequence code division multiple access (DS-CDMA) signals with low signal to noise ratio (SNR) in non cooperative systems is proposed on the basis of analyzing signal model. In the proposed algorithm, a joint posterior distribution model of signal parameters and user numbers is established and samples of distributions to be estimated are obtained by iteratively sampling. The algorithm is able to construct a reversible Markov chain sampler that jumps between parameter subspaces of different dimensionality, so that the posterior distribution of the parameter to be estimated is obtained. Simulation results indicate that the proposed algorithm can be applied to low SNR with equal or unequal power and to different user number. Moreover, the estimation performance of this algorithm has a significant improvement relative to the existing method.