Research On Those Nonlinear Channel Prediction Methods For The Adaptive OFDM Systems Based On Echo State Network

In broadband wireless communication systems,the transmitting system can efficiently transmit the user data by the adaptive OFDM(Orthogonal Frequency Division Multiplexing)systems with some adaptive transmission technologies,such as the adaptive coding and the adaptive modulation.However,the channel state information received in the transmitter is often outdated due to the calculation overhead in the receiver and the feedback delay of the channel state information,when the estimated channel state information is fed back to the transmitter from the receiver.To reduce the influence of the outdated channel state information on the communication system,the future channel state information can be predicted based on the outdated channel state information in the transmitter.Therefore,the channel prediction is an important technology in adaptive OFDM systems.However,the conventional linear channel prediction methods,such as the autoregressive method,have the limited generalization ability for the channel state information with time-frequency-spatial characteristics in adaptive OFDM systems,which limits their channel prediction performances.As a novel reservoir-computing neural network,the echo state network can effectively extract the potential dynamic characteristics of the channel state information in the frequency domain,the time domain and the spatial domain of the adaptive OFDM systems,accurately fit the change trend of the channel state information,and realize the high-precision prediction for the channel state information of the adaptive OFDM systems.Therefore,this dissertation introduces a series of nonlinear channel prediction methods based on the echo state network in the frequency domain,the time domain and the spatial domain,which is significant for the development of the adaptive OFDM wireless communication systems.In this dissertation,we deeply research the time-spatio-frequency characteristics of the OFDM systems,and introduce a series of nonlinear channel prediction methods based on the echo state network.The main research contents of this dissertation include:Firstly,this dissertation introduces a frequency domain channel prediction method based on the adaptive elastic echo state network.To solve the channel prediction issue of the frequency-domian channel state information in the adaptive OFDM systems,the adaptive elastic echo state network is studies and a frequency domain channel prediction method based on the modified adaptive elastic echo state network is introduced in this dissertation.Thereinto,the modified adaptive elastic network is utilized to estimate the output weight matrix,and the ill-conditioned solution issue caused by lots of neurons in the hidden layer is solved effectively.Therefore,the frequency domain channel method based on the modified adaptive elastic echo state network for the adaptive OFDM systems not only has the oracle property and well channel prediction performance,but also can fastly generate the sparse output weight matrix.In the simulations,the modified adaptive elastic echo state network has well one-step channel prediction performance,multi-step channel prediction performance and robustness.Secondly,the significant time-delay tap identification method based on the recursive quantitative analysis is introduced in this dissertation.To solve the problem that it is difficult to distinguish and identify those significant time delay taps and other unsignificant time delay taps in the channel impulse response of the time-domain channel prediction in OFDM systems,a significant time-delay tap identification method based on the recursive quantitative analysis is introduced in this dissertation.The local predictability of every time-delay tap in the channel impulse response is estimated and quantified by the recursive quantization analysis,and the significant time delay tap is distinguished by its local predictability,respectively.Simulation results indicate that the significant time-delay tap identification method based on recursive quantitative analysis has well identification accuracy.Thirdly,a time-domain channel prediction method based on the jointly echo state network is introduced in this dissertation.In the channel impulse response,those significant time delay taps have higher signal noise ratios than other unsignificant time delay taps.To solve the prediction issue of those significant time delay taps,a time-domain channel method for the OFDM systems based on the jointly echo state network is introduced in this dissertation.Thereinto,a double-layer shrink network based on the l_1/2 regularization to estimate the output weight matrix of the echo state network is introduced in this dissertation.The sparsity ability and the generalization ability of the l_1/2 regularization is balanced in this double-layer shrink network,and the ill-conditioned solution issue in the echo state network is solved effectively.This dissertation introduces the computation process of the jointly echo state network,and introduces the implementation process of the double-layer shrink network of estimating the output weight matrix based on the l_1/2 regularization.The simulation results show that the jointly echo state network has better one-step and multi-step channel prediction performances than those classic channel prediction methods,such as AR method.Fourthly,a spatio-domain channel prediction method for the MIMO-OFDM systems based on the broad echo state network is introduced in this dissertation.To solve the issue of the limited sxtraction ability of the single reservoir for the potential dynamic features in the channel state information with spatial characteristic in MIMO-OFDM systems,a spatial domain channel prediction method for the MIMO-OFDM systems based on the broad echo state network is introduced in this dissertation.Thereinto,this dissertation transversely extends the reservoir of the echo state network by the broad learning to improve the ability of echo state network to extract potential dynamic features from the channel state information with spatial characteristics,and realizes the exact prediction for the channel state information of the MIMO-OFDM systems.In the simulation results of different standard scenarios in the 3GPP LTE,different maximum Doppler shifts,different antenna configurations and different spatial correlations,the broad echo state network shows well one-step prediction and multi-step prediction performances.