Multiuser detection based on hidden Markov models
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摘要: 针对以概率统计为基础信道估计收敛速度慢、状态估计存在非合理性的问题进行研究,提出了一种新的多用户检测方法.该算法以隐马尔可夫模型为基础,为了避免由于相邻状态序列后向概率相差很大而造成的信道估计发散,采用固定延迟的方法,利用锯齿延时的办法计算后向序列概率以减少计算复杂度.根据前后序列之间的相关性和最陡下降法,提出了解相关最小均方(DLMS)多用户上行复合信道的盲估计. 考虑到由于发射序列状态之间可能存在时序非继承性,而搜索与前向序列对应的具有最大转移概率的后续序列,给出了最大后验(MAP)多用户检测方法.计算机仿真实验表明,该算法提高了信道响应估计速度,具有全局收敛性和系统的稳定性.Abstract: For investigation on the slow convergence speed of channel estimation and the unreason of state estimation in the probabilistic algorithms, a new multiuser detection was presented. Based on the hidden Markov models, it adopted the fixed-lag to avoid divergence in the estimation channel, and used the sawtooth-lag scheme in calculating the probability of the backward sequence to decrease computational complexity. With the correlation between the previous and the following sequence and the steepest descent algorithm, the new algorithm utilized DLMS to estimate the mutlichannel of the multiuser. Considering seeking for the following sequence of maximal transition probability corresponding to previous sequence, a maximum a posteriori (MAP) multiuser detector had been constructed. The computer simulation illustrates that the new algorithm increase the speed of channel estimation, global convergence and system stability.
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Key words:
- multiuser detection /
- channel estimate /
- hidden Markov model /
- maximum a posteriori
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