Research On Techniques Of Local Area Network For Power Line Communications Based On Perception And Learning

At present,the Power Energy Internet has attracted worldwide attention and its various advantages have been widely recognized by the academic community and industry.Power Line Carrier Communication technology has become one of the main research techniques for Power Energy Internet communication because of its advantages of the transmission of information and power over the same network.However,due to the lack of a sound theoretical system in networking communication and poor communication reliability,Power Line Carrier Communication technology cannot be used for Large-scale practical application.Aiming at improving the reliability of Power Line Communication LAN,the reliability of communication between nodes and the reliability of the PLC-LAN are improved in this paper by the study of Spectrum Sensing technology and Deep Learning technology.Firstly,the characteristics of the low-voltage power line channel are analyzed,and the corresponding channel frequency response model and noise model are established to explore the reasons for poor communicati on reliability between nodes,and a method to improve communication reliability is proposed.At the same time,the basic principle of OFDM technology is elaborated,and the corresponding OFDM power line communication system model is established,which lays a foundation for subsequent research on adaptive methods of node communication channels.Then,in order to improve the reliability of the communication between nodes,a channel-aware channel adaptive algorithm based on spectrum sensing is designed based on the characteristics of the power line channel.The algorithm includes two parts which are spectrum sensing part and channel adaptation part.The characteristics of the algorithm are that it can perceive the spectrum characteristics of the channel and adapt itself based on the perceived spectral characteristics to avoid noise-interfered channels and reducing the error rate of communication,thereby improving the reliability of communication between node.Finally,in order to improve the problem of instability and crash of the Power Line Communication network caused by the sharing of the propagation media,a method of PLC network size control based on Deep Learning is proposed,the main idea of this method is that the network prediction model predicts network results based on existing parameters affecting the networking,such as bit error rate,signal-to-noise ratio,route hops,bandwidth and results of Channel spectrum sensing etc.,and controls the PLC-LAN network size based on the prediction results when a new node joins or changes in networking parameters.The MATLAB simulation platform was used to set up the simulation model of the network prediction based on Stack Auto-Encoder,and the performance of the model was verified by simulation.The results show that the model has high accuracy and can meet the needs of network prediction.