Research On Estimation And Equalization Method Of 5G Communication Channel

In the face of the increasing demand for high data rates,the fifth generation mobile communication technology(5G)has become the frontier technology in the field of wireless communication,as the driving force for wireless communication systems to meet the requirements of ultra-high information transmission rates and large information transmission volumes.Massive multiple input multiple output(m MIMO)technology is introduced as a key technology to fully exploit spatial resources to support high data rates and large channel capacity of 5G networks.Orthogonal frequency division multiplexing(OFDM)technology produces some immunity to frequency selective fading,providing high-speed data access for end users in 5G mobile networks.At the same time,the 5G technology update also leads to a series of problems,how to eliminate interference,how to collaborate dense small intervals,and how to improve system capacity are all challenges to be overcome.The source of solving all the problems is the precise mastery of the transmission channel state information,which requires research and improvement of wireless channel estimation and equalization technology.For massive MIMO systems,channel state information can be obtained by using compressed sensing technology based on channel sparsity.This paper derived the fanaticism of channel estimation algorithm,performance verification,according to pilot problem,raises the compressed sensing channel estimation algorithms,after analyzing the performance of existing reconstruction algorithm,in view of the existing algorithm of signal sparse degree of dependence and fixed step length selection in the limitations of the atom,adopt limited isometric property is used to estimate the sparse degree,The algorithm was optimized by using the correlation coefficient to judge the atomic correlation.The channel equalization technology corrects the distorted signal and restores the true value of the starting signal as much as possible.This paper enumerates the linear equalization channel algorithm widely used in wireless channel and analyzes the advantages and disadvantages of the algorithm by simulation.Then,the adaptive equalization algorithm with real-time performance is introduced.The least mean square(LMS)adaptive equalization algorithm and related improved algorithms are analyzed.The fixed step parameters in the LMS algorithm are optimized by referring to the existing algorithm,and the parameter is associated with the error function to realize the dynamic change of step size.In order to balance the steady-state error and convergence performance of equalization algorithm.