Research On Millimeter Wave MIMO Channel Estimation Based On Compressed Sensing

Massive Multi-input Multi-output technology has become one of the important research directions in the current communication field,and the commercial development of the fifth-generation mobile communication is inseparable from the development of its technology.In the massive MIMO technology,the base station is equipped with a huge number of antenna arrays and uses signal-processing technology to improve the stability of the wireless link and the data transmission rate.In recent years,Compressed Sensing theory has attracted the attention of signal processing because it can sample sparse signals at a rate lower than the Nyquist Sampling Rate.The receiving terminal can use the optimization algorithm to reconstruct the sparse signal from a very limited sample value.Channel estimation is one of the key technologies of massive MIMO communication systems.However,there are problems such as pilot pollution and unsatisfactory channel estimation in MIMO channel estimation.These problems can lead to inaccurate acquisition of channel state information.These problems also restrict the development of massive MIMO technology.In response to the above problems,this article has carried out the following research work.(1)This paper studies the theory of compressed sensing,and apply it to the channel estimation of orthogonal frequency division multiplexing and multiple-input multiple-output(MIMO-OFDM)systems in order to improve Large-scale MIMO system pilot pollution and improved channel estimation performance.A Successive Support Detection broadband beam spatial channel estimation improvement scheme and an Orthogonal Time-Frequency Space modulation-based Orthogonal Matching Pursuit algorithm channel estimation technology scheme are designed.Analysis shows that the designed scheme can greatly reduce the number of pilots and has good estimation performance.(2)Most millimeter wave massive MIMO systems can greatly reduce the number of RF links required through beam selection.However,most existing beam space channel estimation schemes are designed for narrowband systems.To this end,a Successive Support Detection(SSD)broadband beam spatial channel estimation improvement scheme is proposed.Firstly,it is proved that each path component of the channel exhibits a unique sparse structure.Secondly,according to the idea of classical continuous interference cancellation,continuously estimate sparse path components.Finally,for each path component,its channel estimates at different frequencies are jointly estimated by sparse structure and its effects are removed to estimate the remaining path components.The simulation results show that compared with the traditional LS and OMP channel estimation methods,the proposed SSD-based improvement scheme still reduces the pilot overhead and improves the channel estimation accuracy even when the number of paths is unknown.(3)Based on the problem of excessive downlink channel estimation pilot overhead caused by the large number of base station antennas in traditional channel estimation,a channel estimation technique based on Orthogonal Time-Frequency Space modulation and Orthogonal Matching Pursuit algorithm is proposed to solve this problem.Firstly,it proves that the orthogonal time-frequency space channel has the sparsity of the 3D structure;Secondly,based on the channel sparsity of the 3D structure,the downlink channel estimation problem is formulated as a sparse signal recovery problem.The simulation results show that,compared with the traditional impulse scheme and OMP scheme,the massive MIMO channel estimation scheme based on orthogonal time-frequency space can obtain accurate channel state information with lower pilot overhead.The research results show that the designed channel estimation scheme based on the SSD broadband beam spatial channel estimation and the OTFS modulation based on the OMP algorithm can obtain good channel estimation performance.