一种基于因素图的联合信道估计和信号检测迭代算法

The Joint Channel Estimation and Signal Detection Iteration Algorithm via Factor Graph

本文提出了一种新的联合信道估计和信号检测算法.基于广义分配率准则,将实现发送序列最优估计的似然函数分解为多个局部概率函数的乘积,而局部概率函数之间的关系借助直观的因素图来描述.在此基础上,利用和积算法,实现分布式的联合信道估计和信号检测,以较低的计算复杂度达到最优序列检测性能的逼近.同时,采用粒子滤波处理和积算法中出现的连续变量积分,实现频率选择性衰落信道的实时跟踪,从而可有效降低导频数量,提高频谱效率.仿真结果表明,这种联合算法在获得较好的检测性能同时,计算复杂度显著降低.

In this paper,a novel joint channel estimation and symbol detection algorithm is proposed.Based on the idea of generalized distribution law, the likelihood function which is used to achieve the optimal sequence detection is factored into products of several terms of local probability function. An intuitive factor graph is adopted to describe the relationship of these terms. Then a distributive joint channel estimation and symbol detection method is deduced by applying sum-product algorithm on the factor graph directly, which approximates the performance of optimal sequence detection with low computational complexity. Meanwhile, particle filter is introduced to solve integrals of continuous variables in the algorithm, by which a real-time channel tracking is presented. Thus the demand for pilot symbols can be reduced and the spectrum utilization efficiency is improved. Simulation results show that the new algorithm can achieve good performance with low computational complexity.