A-OMP: An Adaptive OMP Algorithm for Underwater Acoustic OFDM Channel Estimation
A-OMP: An Adaptive OMP Algorithm for Underwater Acoustic OFDM Channel Estimation
复制标题
A-OMP：一种用于水下声学 OFDM 信道估计的自适应 OMP 算法
DOI：
10.1109/lwc.2021.3079225
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发表时间：
2021-08
影响因子：
6.3
通讯作者：
Huang Yunlong
中科院分区：
文献类型：
--
作者：
Wang Zhizhan;Li Yuzhou;Wang Chengcai;Ouyang Donghong;Huang Yunlong
应用正交匹配追踪（OMP）算法估计水声正交频分复用（OFDM）信道的一个根本难题是设计合适的迭代终止条件。然而，现有的基于OMP的算法，设计终止条件的基础上的物理稀疏遭受有限的估计精度在高电平噪声的情况下和低电平噪声的情况下的精度和计算成本之间的平衡困难。我们发现，这是恢复稀疏性，一个数量相关，但可能显着不同的物理稀疏性，应利用设计终止条件。基于这一观察，我们首先利用恢复稀疏推导出一个封闭形式的表达式的终止条件，以提高在高电平噪声的情况下的估计精度。然后，为了平衡低水平噪声情况下的估计精度和计算成本，我们设计了另一个终止条件的基础上的残差向量和观测向量。通过将这两个条件嵌入到OMP结构中，我们提出了一种创新的UWA-OFDM通信系统信道估计算法，称为自适应OMP（A-OMP）。仿真结果表明，与OMP相比，A-OMP在所有考虑噪声的情况下都能以较小的计算代价获得相当甚至更高的估计精度。特别地，在高噪声情况下，A-OMP可以在仅13.48%的CPU运行时间的情况下将估计精度提高71.31%。
A fundamentally difficult problem to apply the orthogonal matching pursuit (OMP) to estimate the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel is designing a proper iteration termination condition. However, existing OMP-based algorithms that design termination conditions based on the physical sparsity suffer the limited estimation accuracy in high-level noise cases and the difficult balance between the accuracy and computational costs in low-level noise cases. We find that it is the recovery sparsity, a quantity related to but possibly significantly different from the physical sparsity, that should be utilized to devise termination conditions. Based on this observation, we first exploit the recovery sparsity to derive a closed-form expression for the termination condition to improve the estimation accuracy in high-level noise cases. Then, to balance the estimation accuracy and computational costs in low-level noise cases, we design another termination condition based on the residual vector and the observation vector. By embedding these two conditions into the OMP structure, we propose an innovative channel estimation algorithm for UWA-OFDM communication systems, referred to as the adaptive OMP (A-OMP). Simulation results show that, compared with the OMP, the A-OMP can achieve comparable or even higher estimation accuracy with smaller computational costs in all considered noise cases. Specially, the A-OMP can increase the estimation accuracy by 71.31% with only 13.48% CPU running time in high-level noise cases.