| 王凯,吴立新,张青青.水声通信近似L0范数约束的BP网络均衡器*[J].,2022,41(1):77-83 |
| 水声通信近似L0范数约束的BP网络均衡器* |
| Approximate L0 norm constrained BP neural network equalizer for underwater acoustic communication |
| 投稿时间：2021-03-08 修订日期：2022-01-07 |
| 中文摘要: |
| 针对稀疏水声信道的长时延扩展及梯度下降的权值迭代方案导致的神经网络均衡器收敛速度慢的问题，提出了近似L0范数约束的BP神经网络均衡器。首先在传统BP网络均衡器基础上增加判决反馈项，然后在代价函数中对均衡器输入层到隐含层的权值增加L0范数约束，构造新的代价函数，利用高斯族函数近似L0范数约束，并根据不同隐层神经元节点输出权值的L2范数设定近似参数。仿真结果表明，稀疏信道条件下，本方法相比传统的BP网络均衡器收敛速度更快，误码率更低，可以有效提升神经网络均衡器的性能。 |
| 英文摘要: |
| Aiming at the problem of slow convergence speed of neural network equalizer caused by long delay spread and gradient descent weight iterative scheme of sparse underwater acoustic channel, a BP neural network equalizer with approximate L0 norm constraint is proposed. Firstly, the decision feedback term is added to the traditional BP network equalizer, and then the L0 norm constraint is added to the weight from the equalizer input layer to the hidden layer in the cost function to construct a new cost function. The Gauss family function is used to approximate the L0 norm constraint, and the approximate parameters are set according to the L2 norm of the hidden layer neurons. The simulation results show that, compared with the traditional BP network equalizer, this method has faster convergence speed and lower bit error rate in sparse channel, which can effectively improve the performance of neural network equalizer. |
| DOI：10.11684/j.issn.1000-310X.2022.01.009 |
| 中文关键词: 水声通信 范数约束 神经网络 信道均衡 |
| 英文关键词: Underwater acoustic communication norm constraint neural network channel equalization word two |
| 基金项目: |
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