Abstract: This paper uses the factor graph and sum-product algorithm to discuss the turbo equalization principle, compares the performances of FG-turbo equalization algorithm in the sparse and non-sparse channels, and proposes a new FG-turbo equalization extrinsic information feedback mechanism in the non-sparse channel. Simulation results show that in sparse channel, the performance of the FG-turbo equalization is very close to the performance of the MAP-turbo equalization after several iterations, and the improvement of the modified extrinsic information feedback mechanism makes FG-turbo equalization obtain better performance gain.

Key words: factor graph, sum-product algorithm, turbo equalization, extrinsic information feedback mechanism

摘要： 讨论了基于因子图与和积算法的turbo均衡算法，并对稀疏和非稀疏两种不同信道下的性能进行了比较，提出了稀疏信道下基于因子图的turbo（FG-turbo）均衡改进外信息反馈机制。仿真结果表明，在稀疏信道下，经过若干次迭代FG-turbo均衡的性能逼近MAP-turbo均衡。提出的外信息反馈机制给FG-turbo均衡带来了较大的性能增益。

关键词: 因子图, 和积算法, turbo均衡, 外信息反馈机制