基于扩展路径识别算法的水声OFDM系统低复杂度迭代稀疏信道估计

移动OFDM水声通信系统中，基于压缩感知的稀疏信道估计方法计算量较大，不适用于实时通信。针对这一问题，该文基于一致多普勒信道模型提出一种扩展路径识别(GPI)算法。该方法首先使用信道多普勒扩展矩阵构造等效发射序列，将多普勒信道转化为等效线性时不变信道。然后使用GPI算法估计信道多普勒及各路径的时延及幅度参数，实现低复杂度稀疏信道估计。此外，该文将GPI算法扩展到Turbo接收机中，通过利用信道译码器反馈的数据符号先验信息迭代提高信道估计精度。仿真结果表明，所提方法的性能优于传统的路径识别算法，且与OMP算法接近，而其计算量远低于后者。

Low-complexity Iterative Sparse Channel Estimation for Underwater Acoustic OFDM Systems Based on Generalized Path Identification Algorithm

In mobile OFDM underwater acoustic communication systems, the compressed sensing-based sparse channel estimation methods suffer from high computational complexity, which is not suitable for real-time communication. To solve this problem, this paper proposes a Generalized Path Identification (GPI) algorithm for estimating uniform Doppler distorted channel. This scheme first constructs equivalent transmitted symbols using Doppler spread matrices, and thus the channel is converted into an equivalent linear time-invariant one. Then the GPI algorithm is utilized to estimate the channel parameters. Furthermore, the GPI algorithm is extended to Turbo receivers to iteratively improve the channel estimation accuracy. Simulation results show that the performance of the proposed method is better than that of the conventional path identification algorithm, and is close to the Orthogonal Matching Pursuit (OMP) algorithm. Its computational complexity, however, is much lower than OMP algorithm.