Simulation And Estimation Of 3GPP Time-Varying Channel

With the arrival of the era of Intelligent association of Everything,communication scenarios are more diverse,and users have more and more demands for communication quality.In order to meet these communication requirements,the study of channel characteristics is particularly important.Channel model is the basis of modeling,simulation and evaluation of wireless communication system,so it is very important to establish channel model.The communication scene of the 5th generation mobile communication technology(5th Generation Mobile Communication Technology,5G)and the 6th generation mobile communication technology include the high-speed mobile scene,so the time-varying properties of the channel are highlighted,and the research on time-varying channels is very valuable.The new three-dimensional radio channel model TR38.901 defined by 3GPP for the 5G system fully studies the random characteristics of the 5G channel,and also gives the definition of the timevarying channel model,but there are still areas that need to be supplemented:Simulation and analysis of stationary time-varying channel models and non-stationary time-varying channel models.On the other hand,in order to obtain detailed channel information and demodulate the transmitted signal correctly at the receiving end,the research on channel estimation is also of great significance.In view of the high training cost of traditional channel estimation methods and the ability of recurrent neural networks to solve the time-varying estimation problem well,it is an effective way to use deep learning technology to solve the time-varying channel estimation of 3GPP TR38.901.According to the above background,based on the 3GPP TR38.901 channel model,this thesis completed the modeling and simulation of the stationary time-varying channel model and the non-stationary time-varying channel model,and the characteristics and differences of the two timevarying channel models are compared and analyzed through a variety of simulation results.Meanwhile,based on the two time-varying channel models of 3GPP TR38.901,three time-varying channel estimation methods based on deep learning technology are proposed.Simulation results verify that the proposed scheme can improve the estimation accuracy more than the channel estimation method based on deep neural network(Deep Neural Network,DNN).The above results have certain reference significance for the subsequent research on time-varying channel characteristics.This thesis mainly studies the simulation and estimation of 3GPP TR38.901 time-varying channels.The specific contents are as follows:(1)Modeling and simulation of stationary time-varying channel model and non-stationary time-varying channel model based on 3GPP TR38.901 channel model.According to the different update definitions of channel parameters at different times between the stationary time-varying channel model and the non-stationary time-varying channel model,the modeling and simulation of the two time-varying channel models are completed.By building a time-varying channel simulation platform,follow the procedures of determining the application scenario,antenna mode,system center frequency,channel propagation conditions,and setting the number of scattering paths,generating large-scale and small-scale parameters of the channel,and characterizing random initial phases to complete time-varying channel modeling.Finally,the characteristics and differences of the two time-varying channel models are compared and analyzed according to the simulation results of delay power spectrum,channel impulse response,channel time-frequency correlation transfer function,delay spread cumulative distribution function(Cumulative Distribution Function,CDF),angle spread CDF,time normalized autocorrelation and doppler power spectrum density.(2)Three 3GPP TR38.901 time-varying channel estimation methods based on deep learning technology are proposed.Based on 3GPP TR38.901 stationary time-varying channel model and non-stationary time-varying channel model,three channel estimation methods that can be applied to the two time-varying channel models are proposed:Time-varying channel estimation based on Bi-GRU4Ce model,Time-varying channel estimation based on the Seq2Seq4Ce model,and time-varying channel estimation based on the Transformer4Ce model.The entire time-varying channel estimation process is divided into two stages:training and deployment.In each stage,channel data is collected,processed and verified according to the channel estimation process.Finally,through the simulation results of loss function curve,the normalized mean square error(Normalized Mean Squared Error,NMSE)curve and NMSE curve of the user terminal at different moving speeds,the three channel estimation methods are compared with the DNN-based channel estimation method in the literature,and it is verified that the proposed estimation method can effectively improve the estimation accuracy and reduce the pilot training overhead.