Structured OMP for IRS-Assisted Mmwave Channel Estimation by Exploiting Angular Spread
You You, Li Zhang, Minhua Yang, Yongming Huang, Xiaohu You, Chuan Zhang
Abstract
An emerging technology namely the intelligent reflecting surface (IRS) can be deployed in millimeter wave (mmWave) communication to overcome the large propagation loss and huge power consumption issues. However, the large number of passive IRS elements without signal processing abilities induces high pilot overhead for channel estimation (CE). In this paper, the angular spread feature (or cluster feature) is exploited to formulate the CE problem as a structured sparse signal recovery problem. Then, a structured orthogonal matching pursuit (S-OMP) algorithm is proposed to efficiently solve the problem by utilizing the structure of the channel matrix in row and column without a priori knowledge of the angular spread. Simulation results demonstrate that S-OMP reduces about 40% to 50% pilot overhead at low signal-to-noise ratio (SNR) (SNR= 0 dB) while maintaining the same accuracy of CE compared with the existing methods.