Research On PAPR Reduction And Channel Estimation With High Spectral Efficiency For MIMO-OFDM System

As a key technology of 5G,multiple input multiple output-orthogonal frequency division multiplexing(MIMO-OFDM)technology has been widely studied for its advantages of high system reliability,high channel capacity and simple implementation.Signals are easily distorted by environmental influences when transmitted in wireless channels,so channel estimation is critical for MIMO-OFDM systems.In addition,MIMO-OFDM systems are prone to high peak-to-average power ratio(PAPR)and intermodulation interference of signals.The scarcity of wireless spectrum resources has always been a challenge for wireless communication systems.Therefore,it is of great significance for MIMO-OFDM systems to study PAPR reduction methods and channel estimation methods with high spectral efficiency,simple implementation and accurate estimation.This thesis studies the PAPR reduction methods and the channel estimation methods with high spectral efficiency and high accuracy in MIMO-OFDM systems,which is mainly as follows:(1)Aiming at the problem that the existing PAPR reduction algorithms have large system overhead and unsatisfactory PAPR reduction effect,an optimal threshold active constellation extension(OTACE)algorithm is designed to dynamically find the clipping threshold.Firstly,the constellation expansion is used to increase the distance between the outer constellation points and the adjacent constellation points to enhance the anti-interference ability of the signal.Then,a function about the clipping threshold is constructed to dynamically find the most appropriate clipping threshold at each iteration to further reduce the PAPR of the system.Finally,the number of iterations is experimentally fitted to achieve a balance between algorithm performance and computational complexity.The simulation results show that the proposed algorithm not only reduces PAPR better than the conventional projection onto convex sets(POCS)and smart gradient projection(SGP)algorithms,but also improves the channel estimation accuracy of the system.(2)In view of the problem of low spectral efficiency in wireless communication systems,a channel estimation method based on superimposed pilot and weighted averaging(SP-WA)is designed.Firstly,the scheme of data and pilot superposition is used to improve the spectral efficiency of the communication system.Then,the idea of cancellation is used to remove the interference caused by the superposition of data and pilot in channel estimation.Finally,to improve the accuracy of channel estimation,based on least square(LS)method,weighted averaging is used to suppress additive white Gaussian noise(AWGN)by using the time correlation of wireless channels.The simulation results show that the proposed method has lower normalized mean square error(NMSE)and bit error rate(BER)than multi-frame averaging and LS methods.The superimposed pilot used in this method improves the throughput of the system and saves the frequency band resources than the comb pilot,but the method cannot be used in time-varying channels.(3)For the problem that the previous method SP-WA is not suitable for time-varying channels,a channel estimation method based on Zadoff-Chu(ZC)sequence is designed.To adapt to the time-varying channels,the autocorrelation and cross-correlation characteristics of the ZC sequence are used to transmit the ZC sequence as pilots to the wireless channel,and the linear minimum mean square error(LMMSE)method is used to estimate the channel at the receiver.The simulation results show that the accuracy of channel estimation using ZC sequence is higher than that of random pilot,and because of the good correlation of ZC sequence,higher channel estimation accuracy can be obtained when fewer ZC sequence are inserted,which improves the spectral efficiency of the system.