基于双路径网络的矿井无线信号检测方法的研究

李旭虹; 李彤彤; 王安义

doi:10.13272/j.issn.1671-251x.2022100052

基于双路径网络的矿井无线信号检测方法的研究

doi: 10.13272/j.issn.1671-251x.2022100052

西安科技大学通信与信息工程学院, 陕西西安　710600

基金项目: 国家自然科学基金联合基金资助项目(U19B2015)。

详细信息

作者简介:
李旭虹（1970—），女，新疆乌鲁木齐人，副教授，研究方向为通信电路与系统技术、无线通信，E-mail：lixhong105@xust.edu.cn

通讯作者:
李彤彤（2001—），女，河南汝州人，硕士研究生，研究方向为智能信息处理、移动通信，E-mail：918213549@qq.com

中图分类号: TD655
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出版历程
- 收稿日期: 2022-10-18
- 修回日期: 2023-05-15
- 网络出版日期: 2022-12-13

Research on mine wireless signal detection method based on dual path network

College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710600, China

摘要

摘要: 目前针对矿井无线信号检测的研究大多只考虑了比较理想的加性高斯白噪声信道和瑞利衰落信道，且信号检测误码率高，网络结构复杂。针对上述问题，提出一种基于双路径网络（DPN）的矿井无线信号检测方法，采用双路网络接收机（DPNR）优化正交频分复用（OFDM）接收端的整体性能，解决常规接收机的误差累积问题。首先采用残差（Res）块的shortcut对浅层特征进行一次卷积，将经过一次卷积后的特征图与经过多次卷积后的特征图相加。然后将密集（Dense）块浅层重复利用，并进行Dense块的卷积计算，得到卷积计算后的特征图。最后将两者的特征图融合成新的特征图，在牺牲较少复杂度的情况下，提取更多的特征，从而提高检测性能。实验结果表明：① 在OFDM系统中，DPNR的误码率比常规接收机低，在信噪比为13时，误码率为零；在信噪比大于7 时，DPNR的误码率较矿井环境下的常规接收机降低1个数量级以上；在信噪比大于11时，DPNR的误码率较加性高斯白噪声下的常规接收机降低 1个数量级以上。② 在通信系统滤波器组多载波/偏置正交幅度调制中，DPNR的误码率较常规接收机的降低2个数量级以上，说明其具有较好的鲁棒性。③ 随着信噪比的增加，DPNR和残差神经网络（ResNet）接收机的误码率较密集连接卷积网络（DenseNet）接收机低，且DPNR的误码率在最后阶段即信噪比大于13时更低。④ 在较高信噪比情况下，DPNR的误码率远远低于深度接收机，在信噪比大于8时，DPNR的误码率较深度接收机降低1个数量级以上。
- 矿井无线通信 /
- 无线信号检测 /
- 误码率 /
- 双路径网络 /
- 双路网络接收机 /
- 信噪比 /
- 常规接收机 /
- 深度接收机
Abstract: At present, most of the research on mine wireless signal detection only considers the ideal additive Gaussian white noise channel and Rayleigh fading channel. The signal detection has high bit error rate and complex network structure. In order to solve the above problems, a mine wireless signal detection method based on dual path network (DPN) is proposed. The method uses dual path network receiver (DPNR) to optimize the overall performance of the orthogonal frequency division multiplexing (OFDM) receiver and solve the problem of error accumulation in conventional receivers. Firstly, the residual (Res) block's shortcut is used to perform a convolution of shallow features, and the feature map after one convolution is added to the feature map after multiple convolutions. Secondly, the shallow layer of the Dense block is reused. The convolution calculation of the Dense block is performed to obtain the feature map after the convolution calculation. Finally, the feature maps of the two are fused into a new feature map, which extracts more features at the expense of less complexity, thereby improving detection performance. The experimental results show the following points. ① In OFDM systems, the bit error rate of DPNR is lower than that of conventional receivers. When the signal-to-noise ratio is 13, the bit error rate is zero. When the signal-to-noise ratio is greater than 7, the error rate of DPNR is reduced by more than one order of magnitude compared to conventional receivers in mine environments. When the signal-to-noise ratio is greater than 11, the bit error rate of DPNR is more than one order of magnitude lower than that of conventional receivers under additive Gaussian white noise. ② In the multi-carrier/offset orthogonal amplitude modulation of communication system filter banks, the error rate of DPNR is reduced by more than two orders of magnitude compared to conventional receivers, indicating its good robustness. ③ As the signal-to-noise ratio increases, the bit error rate of DPNR and residual neural network (ResNet) receivers is lower than that of densely connected convolutional networks (DenseNet) receivers. The bit error rate of DPNR is lower in the final stage when the signal-to-noise ratio is greater than 13. ④ At higher signal-to-noise ratios, the bit error rate of DPNR is much lower than that of deep receivers. When the signal-to-noise ratio is greater than 8, the bit error rate of DPNR is reduced by more than one order of magnitude compared to deep receivers.
- wireless communication in mines /
- wireless signal detection /
- error rate /
- dual-path network /
- dual network receiver /
- signal-to-noise ratio /
- conventional receiver /
- deep receiver

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图 1 不同m值下Nakagami−m模型的概率密度函数

Figure 1. The probability density function of Nakagami-m model under different m values

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图 2 基于深度接收机的OFDM无线通信系统

Figure 2. Deep receiver-based OFDM wireless communication system

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图 3 DPNR网络架构

Figure 3. The DPNR network architecture

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图 4 DPNR中 DPN块的网络

Figure 4. Network diagram of DPN block in DPNR

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图 5 不同个数DPN块的误码率

Figure 5. Error rate of DPN blocks with different numbers

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图 6 OFDM接收机的误码性能对比

Figure 6. Error performance comparison of OFDM receivers

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图 7 FBMC/OQAM接收机的误码性能对比

Figure 7. Error performance comparison of FBMC/OQAM receivers

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图 8 不同网络下的误码性能对比

Figure 8. Comparison of BER performance in different networks

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图 9 DPNR与深度接收机的误码性能对比

Figure 9. Comparison of BER performance between DPNR and deep receivers

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表 1 DPNR网络节点的卷积设置

Table 1. Convolution settings for the DPNR network nodes

网络块	输出	卷积设置
Cov1	(None,160, 64)	[31,64]×1
过渡块	(None,79,128)	[5,128]×1
DPN块	(None,79,320)	[1,128]×1,[5,128]×1
过渡块	(None,39,64)	[5,64]×1
DPN块	(None,39,224)	[1,128]×1,[5,128]×2
过渡块	(None,19,64)	[5,64]×1
DPN块	(None,19,288)	[1,128]×1,[5,128]×3
过渡块	(None,9,64)	[5,64]×1
DPN块	(None,9,224)	[1,128]×1,[5,128]×2
Cov	(None,9,150)	[5,150]×1
全连接层	(None,9,150)	−

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表 2 OFDM参数的设置

Table 2. OFDM parameter settings

参数	值
FFT点数	64
CP长度/chip	16
子载波	52
调制	QPSK
信道	Nakagam−m+AWGN

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表 3 网络架构初始参数

Table 3. Initial parameters of the network architecture

参数	值
优化器	Adam
学习率	10⁻³
批大小	256
训练次数	15
训练数据集数量	320 000
测试数据集数量	320 000

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