Modeling And Estimation Of Double Sparse Channels For Massive MIMO-OFDM Systems

Communication systems need to meet larger capacity and higher transmission rates with the explosive growth of mobile business data.Large-scale MIMO system has become an important research direction for next-generation wireless communications since it can obtain greater gain and system capacity from beamforming and spatial multiplexing of multi-antenna arrays.Taking millimeter waves as an example,it is highly susceptible to shadows due to its high frequency.Millimeter-wave massive MIMO channels do not follow the traditional rich scattering model because the number of scatters is limited in this environment.It is especially important to establish statistical channel model and design channel estimation method according to the sparse transmission characteristics of time domain and angular domain in large-scale MIMO systems.In this paper,a time domain and angular domain double-sparse multipath channel model is established based on massive MIMO systems,and channel estimation is performed by compressed sensing and Iterative retesting super resolution methods.The main research work is summarized as follows:(1)The angular domain characteristics of the massive MIMO system are analyzed based on the physical channel of wireless communication.And the angular domain representation of the narrowband MIMO channel is obtained.Then the correlation of the spatial feature maps of different angle signals,the dependence of the angular window on the antenna spacing,the angular gain and the anti-interference of the narrowband MIMO channel are studied.The simulation results show that the resolution and anti-interference ability of the angular domain are enhanced with the increase of the antenna in the system.Last but not least,the multi-path channel model of time-angular domain double sparse in large-scale MIMO broadband system is obtained,which is the cornerstone of the channel estimation method.(2)An estimation method based on compressed sensing for double sparse multipath channel is designed.In the large-scale MIMO broadband system,the channel is sparse in the time domain and the angular domain.In this paper,the OMP algorithm performs denoising processing in the time domain and the angular domain respectively.The experimental results show that the performance of considering the double sparsity of the time domain and the angular domain during channel transmission is better than that of the traditional time domain sparse channel,and the former's ability to suppress noise is stronger.In addition,the effects of different sparsity and different antenna numbers on channel recovery performance are simulated.(3)A super-resolution dual sparse channel estimation scheme based on iterative retest is designed.Each subcarrier channel can be considered a narrowband channel in MIMO-OFDM systems.In this paper,the optimization method is used to gradually estimate the path angle from the initial angular domain grid to its actual angular position to achieve super-resolution channel estimation.And the position where the channel gain is too small is usually only noise,and the estimated sparsity level will approach the actual number of paths by iteratively trimming.The simulation results show that the algorithm can further improve the accuracy of channel estimation under certain conditions.