Deep Learning based Channel Estimation in PLC Communication

JING Tiancheng; DUAN Hongguang; ZHAO Xu; ZHANG Jiaxin

doi:10.13756/j.gtxyj.2024.220058

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Deep Learning based Channel Estimation in PLC Communication

更新时间：2024-04-02

- Deep Learning based Channel Estimation in PLC Communication
- Study on Optical Communications Issue 2, Pages: 22005801-22005808(2024)
- 作者机构：
  
  1.重庆邮电大学　通信与信息工程学院，重庆　400065
  2.北京智芯微电子科技有限公司，北京　102200
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2024.220058
  CLC： TN915.853
- Published：10 April 2024，
  
  Received：27 February 2023，
  
  Revised：21 March 2023，
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敬天成，段红光，赵旭，等. 电力线载波通信中基于深度学习的信道估计[J].光通信研究，2024(2):220058.

Jing T C, Duan H G, Zhao X, et al. Deep Learning based Channel Estimation in PLC Communication [J]. Study on Optical Communications, 2024(2):220058.
敬天成，段红光，赵旭，等. 电力线载波通信中基于深度学习的信道估计[J].光通信研究，2024(2):220058. DOI： 10.13756/j.gtxyj.2024.220058.

Jing T C, Duan H G, Zhao X, et al. Deep Learning based Channel Estimation in PLC Communication [J]. Study on Optical Communications, 2024(2):220058. DOI： 10.13756/j.gtxyj.2024.220058.

摘要

【目的】

电力线载波（PLC）通信系统采用基于帧突发的传输模式，由于PLC系统的收发信机之间存在载波频偏、PLC信道存在各种噪声以及时变特性，加之PLC系统没有专用参考信号，传统信道估计对PLC信道没有跟踪预测能力，进而造成PLC系统性能恶化。

【方法】

文章针对现有问题，提出了一种基于长短期记忆（LSTM）神经网络和去噪卷积神经网络（DnCNN）的去噪长短期记忆（DnLSTM）神经网络，并利用该DnLSTM神经网络进行了PLC信道估计。首先对DnLSTM神经网络进行离线训练再保存训练好的DnLSTM参数，之后将其部署到PLC系统中，加载训练完成的参数后再进行在线预测，得到PLC系统信道响应。在电力线系统仿真中，文章采用最小二乘法（LS）、最小均方误差（MMSE）算法以及DnLSTM神经网络进行信道估计，给出在高斯白噪声（AWGN）、组合噪声、色噪声和脉冲噪声条件下的仿真结果，同时调整了用于信道估计的前导符号数量并进行了对应的仿真。

【结果】

仿真结果表明，DnLSTM神经网络进行信道估计的精度与采用的前导符号数量有关，采用4个前导符号进行信道估计，其估计精度优于LS，接近MMSE算法，并且DnLSTM神经网络具有很好的抵抗载波频偏以及信道时变的能力。当用于信道估计的前导符号越多时，低信噪比（SNR）情况下的PLC系统性能越好，高SNR情况下的PLC系统性能相似。

【结论】

通过以上仿真可得出，基于LSTM和DnCNN的DnLSTM神经网络可以很好地估计存在频偏的PLC系统信道响应，可实时跟踪其变化。

Abstract

【Objective】

Power Line Carrier(PLC)communication adopts the frame burst transmission mode. Due to the carrier frequency offset between transceivers

various noise and time-varying characteristics of PLC channel and the system has no dedicated reference signal. The traditional channel estimation has no tracking and prediction ability for the channel

which leads to the deterioration of the PLC system performance.

【Methods】

Aiming at the existing problems

this paper proposes a Denoising Long Short Term Memory (DnLSTM) neural network based on Long Short Term Memory (LSTM) neural network and Denoising Convolutional Neural Network (DnCNN)

which is used for PLC channel estimation. First

offline training is performed on DnLSTM and the parameters are saved after training. Then the trained parameters are deployed in PLC system. After loading parameters

online prediction is performed to obtain the predicted PLC system channel estimation. In the simulation of PLC system

this paper uses Least Squares (LS) algorithm

Minimum Mean Square Error (MMSE) algorithm and DnLSTM to estimate the channel response

and gives the simulation results under the conditions of Additive White Gaussian Noise (AWGN)

combined noise

impulsive noise and colored noise. Meanwhile

simulations for different number of preamble symbols for channel estimation are performed.

【Results】

The results show that there is a relationship between the accuracy of DnLSTM channel estimation and the number of preamble symbols. Using four preamble symbols for channel estimation

its estimation accuracy is better than LS and close to MMSE algorithm. DnLSTM has a good ability to resist carrier frequency offset and channel time-varying. When the number of preamble symbols for channel estimation increases

PLC system performance with low Singnal to Noise Ratio (SNR) gets better and PLC system performance is similar with high SNR.

【Conclusion】

According to simulation results above

it can be concluded that DnLSTM

which is based on DnCNN and LSTM

can predict PLC system channel response with frequency offset very well and it can track the varying PLC system channel response in real time.

关键词

电力线载波通信信道估计深度学习长短期记忆神经网络去噪卷积神经网络

Keywords

PLC communicationchannel estimationdeep learningLSTM neural networkDnCNN

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School of Information Science and Technology, Southwest Jiaotong University

College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications

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State Key Laboratory of Advanced Optical Communication System and Networks, Shanghai Jiao Tong University

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