Low complexity iterative MLSE equalization in highly spread underwater acoustic channels
Low complexity iterative MLSE equalization in highly spread underwater acoustic channels
复制标题
高度分散的水声通道中的低复杂度迭代 MLSE 均衡
DOI：
10.1109/oceanse.2009.5278300
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Jan C. Olivier
中科院分区：
文献类型：
--
作者：
H. Myburgh;Jan C. Olivier
本文在前人工作的基础上，提出了一种基于近似最优硬输出神经网络的迭代最大似然序列估计（MLSE）均衡器，能够均衡具有超长时延扩展的水声信道中的单载波4-QAM信号。所提出的均衡器的性能进行比较，一个次优均衡技术，即判决反馈均衡（DFE），通过计算机模拟的功率延迟配置文件的数量。结果表明，无与伦比的性能在一小部分的计算成本的最佳，但不切实际的，均衡方法。该均衡器的上级计算复杂度是由于其基本神经网络结构的高度并行性和高水平的神经元互连。
This work proposes a near-optimal hard output neural network based iterative Maximum Likelihood Sequence Estimation (MLSE) equalizer, based on earlier work by the authors, able to equalize single carrier 4-QAM signals in underwater acoustic channels with extremely long delay spreads. The performance of the proposed equalizer is compared to a suboptimal equalization technique, namely Decision Feedback Equalization (DFE), via computer simulation for a number of power delay profiles. Results show unparalleled performance at a fraction of the computational cost of optimal, yet impractical, equalization methods. The superior computational complexity of the proposed equalizer is due to the high parallelism and high level of neuron interconnection of its foundational neural network structure.