基于OS-ELM的宽带电力线通信解映射优化算法

针对传统信道估计技术会降低通信系统有效性的缺陷,提出了一种基于在线序贯极限学习机(OS-ELM)的宽带PLC解映射优化算法,用以提高宽带电力线通信系统的通信质量。以我国广东省某小区用户电能表的实际采集数据作为原始数据,搭建了宽带电力线通信系统仿真模型,在实测的500 m四径信道下进行仿真测试并与BP神经网络以及传统的ELM进行性能对比和比较分析。试验结果表明,在各种不同信噪比的通信环境下,引入OS-ELM均表现出更快的训练速度和更好的抗干扰特性。除去信噪比过低的极端恶劣的通信环境以外,该算法均可以有效提高通信质量,降低误码率。

Optimization algorithm for de mapping module of wideband power line communication based on OS-ELM

In order to ensure the reliability of the communication, the channel estimation technique is widely used in the OFDM system to reduce the bit error rate in the traditional broadband power line communication system. However, due to the need of importing a large number of pilot sequences, the channel estimation technology will take up valuable spectrum resources. Its implementation process is complex and will greatly reduce the effectiveness of communication. Therefore, we proposed a optimization algorithm of de mapping module for broadband PLC based on the Online Sequential-Extreme Learning Machine (OS-ELM). This algorithm is an online learning improvement algorithm of the traditional Extreme Learning Machine (ELM), which can combine batch processing and successive iteration to update training data and network parameters. This paper sets up a simulation model of the broadband power line communication system based on the actual data collected from the user electric meters in a residential district of Guangdong province. The simulation test is carried out under the measured 500m four-path channel and compared with the BP neural network and the traditional ELM. The experimental results show that the introduction of OS-ELM in a variety of SNR communication environments shows faster training speed and better anti-interference characteristics. Except for the extremely poor communication environment with low SNR, the algorithm can effectively improve the communication quality and reduce the bit error rate.