Research On Channel Estimation Algorithm For Millimeter Wave Massive MIMO System

As we transition into the fully networking society,the number of wireless communication connected devices is rapidly increasing,and the demand for high-speed and large-capacity transmissions will be higher in the future.The development of millimeter-wave Massive MIMO wireless communication will bring about substantial changes to solve the above problems.Highspeed data transmission,effective antenna array gain,and anti-path attenuation will all be improved in the future.However,in the millimeter-wave Massive MIMO system,configuring a large number of antenna elements and radio frequency(RF)links at the base station will lead to increased system hardware complexity and excessive system energy consumption.At the same time,the number of antennas at the base station side of the system increases.The complexity of the channel estimation.In order to solve some difficult problems in channel estimation,this topic combines beam selection and channel estimation.The main work of this article is as follows:(1)Firstly,we briefly discuss the research status of millimeter wave,beamforming,and channel estimation,and learn about millimeter-wave Massive MIMO.Then explain the relevant knowledge of beamforming,describe the IA beam selection scheme,and perform simulation analysis.(2)Obtaining accurate real-time and valid state information is a prerequisite for evaluating system performance.The channel estimation model is first described,channel characteristics are analyzed,traditional MMSE and LS channel estimation algorithms are listed,and their characteristics are analyzed.Then analyzes the sparse characteristics of Massive MIMO channel,combined with Compressed Sensing(CS)channel estimation theory,simulates OMP algorithm and CoSaMP algorithm,analyzes the advantages and disadvantages of the two algorithms,and compares the traditional MMSE algorithm with more advantages.However,under low SNR,if the beamforming gain is not obvious or the user transmit signal power is low,the above CS algorithm performance is usually poor,the system signal is easily masked by various noises,and the non-zero elements of the classical CS algorithm Most of the location is estimated using an iterative approach,with high computational complexity,and it will be estimated that it will become increasingly inaccurate as the amplitude of non-zero elements decreases.To solve the above problems,a channel estimation scheme based on adaptive channel detection(ASD)is proposed.The simulation results show that the performance of the proposed algorithm is better than that of the classical CS channel estimation algorithm.(3)Massive MIMO system has more serious multi-user interference,leading to a decrease in pilot utilization rate,which is also unfavorable to channel estimation,and seriously affects channel information transmission efficiency and system performance.In order to solve this problem,considering the TDD system model,the joint beamforming technology proposes an ASD-RVQ channel estimation algorithm based on beam selection for millimeter-wave Massive MIMO.Based on the sparse characteristics of the beam space channel,the beam signal is selected using the maximization of amplitude(MM)standard based on RZF precoding to mitigate multi-user interference,and then sparse sparse beam space is processed with high-accuracy adaptive support detection(ASD).The signal vector is then subjected to random vector quantization(RVQ)on the processed channel vectors,thereby obtaining a channel matrix with a reduced system dimension to estimate the entire channel state information,and finally estimating the non-zero elements of the channel using the LS algorithm.Simulation results verify that the proposed scheme can effectively improve system performance and energy efficiency on the basis of reducing the number of system RF links.