Demodulation of Downhole Wireless Electromagnetic 2FSK Signals Based on Stochastic Resonance

The noise interference caused by the electric appliances at and around well site has an impact on the received wireless electromagnetic 2FSK modulation signals,making it difficult to extract signal features when the signal-to-noise ratio(SNR)is low. Therefore,an adaptive bistable stochastic resonance system based on the coati optimization algorithm(COA)was proposed to reduce the bit error rate of 2FSK signals. This method takes full advantage of the global exploration and local optimization balance ability of COA to conduct selection and optimization of multiple parameters of the stochastic resonance system,allowing the system output to have the maximum SNR gain. Then,the convolutional neural network(CNN)was used to demodulate the output signals of the stochastic resonance system and evaluate their bit error rate. The simulation and test results show that under low SNR conditions,the characteristic frequency of output signals of the stochastic resonance system based on COA is more significant than that of the ant colony optimization(ACO)algorithm,and has a lower bit error rate. The research results provide technical support for the real-time transmission of downhole signals.