Research On Massive MIMO Channel Estimation Based On Continuous Domain Convex Optimization

Massive MIMO(Massive Multiple-Input Multiple-Output)has the advantages of strong anti-interference ability,high spectrum efficiency,and high system capacity.Due to the short wavelength of mm Wave,a large number of antennas can be deployed in a small area,so the combination of the two has great advantages.At the same time,the hybrid Massive MIMO structure has received widespread attention because it can significantly reduce the number of radio frequency chains in the system,thereby reducing the hardware cost and power consumption of the system structure.However,compared with the traditional array antenna structure,the hybrid Massive MIMO system can only receive a set of reduced-dimensional data due to the reduction of the radio frequency chains,which makes it necessary to solve the underdetermined equations.In response to the above problems,researchers have proposed many channel estimation algorithms by taking advantage of the sparse feature of the channel.However,since such algorithms require preset dictionary,it will lead to the problem of basis mismatch,which seriously limits the performance of the algorithm.Although the atomic norm method avoids the occurrence of basis mismatch problems,this method has threshold requirements for the frequency interval of the estimated signal.In addition,the problem of pilot overhead in the channel estimation process has always plagued researchers,so it is necessary to study the channel estimation algorithm.In this thesis,focusing on the hybrid Massive MIMO structure,a gridless channel estimation algorithm is proposed for the channel estimation problem under underdetermined conditions.Focusing on the traditional Massive MIMO structure,a downlink channel estimation algorithm based on reconstructed Hankel matrix is proposed.The main content is as follows:First,the channel model and hybrid Massive MIMO structure under millimeter wave are studied,and the classical algorithm of traditional MIMO channel estimation is analyzed.After that,the advantages and disadvantages of the existing channel estimation algorithms are analyzed.Then,the hybrid Massive MIMO uplink channel estimation problem is studied,and an uplink channel estimation algorithm based on reconstructed Hankel matrix is proposed.The algorithm is a gridless method,which can solve the basis mismatch problem caused by the traditional compressed sensing method.The algorithm first obtains the received data at the base station,then designs an optimization function problem based on nuclear norm minimization,and obtains the estimated value of the channel vector by solving the optimization problem.And the algorithm obtains the noise parameters of the base station receiver,which further improves the channel estimation accuracy.In addition,there is no need to consider the frequency interval threshold of the signal to be estimated when applying the algorithm,which can effectively expand the applicable range of the algorithm.Theoretical analysis of uniform linear array and non-uniform linear array is made respectively in this thesis.The simulation results show that the algorithm has advantages under the conditions of different signal-to-noise ratio and the number of radio frequency chains.Finally,the downlink channel estimation problem of Massive MIMO is studied,and a downlink channel estimation algorithm based on reconstructed Hankel matrix is proposed.The algorithm does not need to set up dictionary,which solves the problem of basis mismatch and excessive pilot overhead of existing algorithms.Firstly,the received data and channel noise parameters are obtained at the user side.Secondly,the corresponding Hankel matrix is constructed.At the same time,the above mentioned received data is used in the construction process of constraint conditions,and the corresponding nuclear norm minimization problem is solved to obtain the estimated value of the channel vector.Theoretical analysis of uniform linear array and non-uniform linear array is made respectively in this thesis.The simulation results show that,compared with the traditional algorithm,the algorithm can significantly reduce the pilot overhead of downlink channel estimation,and at the same time has higher estimation accuracy.