Remodelled and Reduced complexity-OMP-based Channel Estimation Schemes for Intelligent Reflecting Surface-Aided Millimeter Wave Systems

Intelligent reflecting surface (IRS) has been put forward as one of the key candidate technologies to achieve high spectrum efficiency. However, to reach its full potential there is a need for the availability of accurate channel state information, which is quite challenging to estimate. This paper, therefore, focuses on proposing two compressive sensing algorithm-based channel estimation schemes to be used in the IRS-aided millimeter wave (mmWave) wireless communication systems. The first channel estimation named reduced complexity-orthogonal matching pursuit (OMP) (RedCOMP)-based channel estimation scheme seeks to achieve optimum performance while reducing the computational complexity associated with the traditional OMP algorithm. The second scheme named remodelled OMP (RemOMP)-based channel estimation method aims at obtaining enhanced channel estimate with the aid of side information known a priori at the receiver. Simulation results indicate that the proposed RemOMP-based channel estimator exhibits the best performance among the three channel estimation schemes with similar computational complexity cost to the traditional OMP-base channel estimator. However, the RedCOMP-based channel estimation scheme, while not showing good performance, incurred the least computational complexity cost among all the schemes.