一种新颖的异步多径CDMA信号的 贝叶斯多用户检测方法

马尔可夫链蒙特卡罗(MCMC)方法有效地解决了贝叶斯计算的问题,但是不容易将它应用于有未知干扰用户的异步多径CDMA系统.为了克服这一困难,本文提出一种新颖的贝叶斯多用户检测方法,它首先用线性群盲解相关器对接收信号做预处理,然后再用Gibbs采样(一种典型的MCMC算法)做贝叶斯多用户检测.仿真结果表明,该方法的检测性能明显地优于线性群盲多用户检测,其计算复杂度的增加与小区内用户数目呈线性关系.为了进一步提高本文方法的性能,我们使用两级Gibbs采样,根据第一级Gibbs采样的输出得到更精确的参数估计,并把它用于第二级Gibbs采样中.仿真结果证明,与只使用一级Gibbs采样的方法相比,两级Gibbs采样的检测性能明显地改善了.

A Novel Bayesian Multiuser Detection Method for Asynchronous CDMA Signals in Multipath Channels

Markov chain Monte Carlo (MCMC) methods are considered as the powerful techniques for Bayesian computation.However,it is difficult to apply them to the asynchronous CDMA systems in the presence of unknown interferences and multipath fading.In this paper,a novel Bayesian multiuser detection method is proposed to overcome the difficulty.In the proposed method,the received signal is preprocessed at first by the linear group-blind decorrelator,and then,the Gibbs sampler (a typical MCMC procedure) is employed to perform the Bayesian multiuser detection.Simulation results show that this method significantly outperforms the linear group-blind multiuser detection with a low additional complexity linear with the number of intra-cell users.To improve the performance of the proposed method,two stage Gibbs samplers are used.In the first stage,the parameter estimation with high accuracy is achieved based on the output of the Gibbs sampler and the estimated parameters are used in the Gibbs sampler of the second stage.It is seen that the detection performance is improved in comparison with that of the method using one stage Gibbs sampler.