Research On Joint Estimation Method Of Detection Noise And Turbulent Channel Parameters

Free space optical(FSO)communication is susceptible to time-varying atmospheric channels,and the effectiveness and reliability of communication greatly depend on the cognition of channel state.In the wireless laser adaptive system,in order to adjust the transmission parameters in real time to adapt to the time-varying atmospheric channel and achieve an effective balance between transmission performance and transmission rate,it is necessary to estimate the physical state information of the atmospheric channel at the receiving end.In the process of receiver photoelectric detection,the received signal will contain background light,detector background noise(or shot noise)and circuit thermal noise,which will affect the estimation of atmospheric channel fading parameters.Therefore,it is of great significance to carry out joint estimation of photoelectric detection noise and atmospheric turbulence channel fading parameters,and to study the influence of detection noise on atmospheric channel fading parameters.In this paper,the traditional maximum likelihood joint estimation and deep learning joint estimation methods are studied for the detection noise and turbulent channel fading parameters.The performance of the proposed estimation methods is evaluated and verified by the measured detection noise and channel data under different weather conditions.The specific contents include:1.The basic concepts of atmospheric turbulence and photoelectric detection noise are expounded.The channel estimation technology and deep learning technology are summarized.The advantages and disa.dvantages of turbulent channel parameter estimation based on Gamma-Gamma atmospheric turbulence model are analyzed.The joint probability density distribution of Gamma-Gamma atmospheric turbulence channel and photoelectric detection noise is established.Based on this distribution,the maximum likelihood joint estimation expression of Newton-Raphson(N-R)method and expectation maximization(EM)algorithm is derived.2.A maximum likelihood joint estimation algorithm for detecting noise and turbulent channel fading parameters is proposed.A joint channel estimation method of N-R method and EM algorithm is established for detection noise and turbulent channel fading,and Cramer-Rao Bound(CRB)is introduced to evaluate the performance of the designed estimator.Based on the turbulence channel simulation data,the Mean-Square Error(MSE)is used to compare and analyze the performance of the estimator considering and not considering the influence of photoelectric detection noise.Based on the measured channel and detection noise data under three weather conditions,it is verified that the proposed algorithm can significantly improve the estimation accuracy of atmospheric turbulence fading parameters considering the influence of detection noise.3.A joint channel estimation method of detection noise and turbulent channel based on neural network is proposed.The training data is generated by using the relationship between the detection noise and the turbulent channel,and the noise parameters and channel parameters are used as labels to match the data one by one,and then a fixed window is set to scan the one-dimensional data into two-dimensional data.The joint estimation network model of detection noise and channel fading of Convolutional Neural Network(CNN)is established.The feature information of detection noise and turbulent channel is trained and learned by convolution,pooling,adaptive learning rate and regularization,so as to obtain high-precision atmospheric channel estimation features.The network model is adjusted from the number of convolution layers,data sample size and network hyperparameters,and the influence of parameters such as channel parameters and noise variance on the estimation performance is explored.The parameter estimation effects of EM algorithm and CNN model under the measured data set are compared.It shows that the model has strong channel characteristic extraction ability for different atmospheric environments,and presents an ideal estimation effect,which solves the problem of accurately estimating channel parameters in complex atmospheric environments from noise-contaminated fading signals.