| dc.description.abstract | Millimeter-wave (mmWave) communication is an emerging candidate technology in the fifth generation (5G) mobile communication, but mmWave communication still has challenges to face, such as limited coverage, the need to build many base stations to transmit signals, thus incurring costs expensive, only supports line-of-sight transmission, etc. In recent years, based on the above problems, scholars have developed a new technology called intelligent reflecting surface (IRS) technology, which intelligently reconstructs the wireless transmission environment through a large number of low-cost passive reflective elements, thereby improving the performance of wireless communication. In addition, due to the high path loss caused by mmWave channels, massive MIMO antenna technology is required to compensate by beamforming gain. However, it is not easy to actually simulate massive MIMO antenna technology for mmWave channels. And the high cost problem caused by the radio frequency (RF) chain needs to be considered. Therefore, scholars have proposed a hybrid beamforming structure and applied a solution to reduce energy loss and implementation cost. Therefore, scholars have proposed a hybrid beamforming structure as a solution to reduce the cost and energy loss of implementation. Assuming we are given perfect channel state information (CSI), we can design hybrid precoders and combiners and transmit data in mmWave channels. However, if the channel state information (CSI) cannot be known, we need to estimate the channel in advance. Therefore, many channel estimation methods have been proposed. In this paper, for the mmWave channel estimation problem of hybrid architecture, we not only apply the sparse nature of mmWave channels, but also combine the compressed sensing technology to estimate the sparse channels, and use the projection algorithm to predict the digital baseband and analog radio frequency. The encoder design problem reduces to a sub-optimization problem where the optimal solution can be found. In conclusion, the algorithm proposed in this paper is applied to mmWave massive MIMO system, which considers the solutions of channel estimation, beamforming and IRS phase shift optimization, and the spectral efficiency is close to that achievable with perfect channel state information. | en_US |