Channel Estimation In Intelligent Reflecting Surfaces for 5G and Beyond

Abstract

Intelligent Reflecting Surfaces (IRS) are constructed of multiple independently controllable passive Reflecting Elements (RE), which can change the phase and amplitude of the reflected signals so that the reflected signals can be combined in coherent manner to achieve beamforming. To facilitate beamforming, the channel coefficients of the incoming and outgoing channels need to be estimated. In this study, the Discrete Fourier Transform (DFT) based channel estimation method is applied to an IRS-assisted communication system implementing Fifth Generation (5G) Orthogonal Frequency Division Multiplexing (OFDM) waveform in order to observe the effectiveness of the estimation method. DFT-based channel estimation has the advantage of not using the whole OFDM symbol for pilot transmission, thus it can be performed while transmitting data. Therefore, the effects of multipath delay spread, the number of REs, and training sequence sparsity in the OFDM symbol are observed for different Signal-to-Noise Ratio (SNR) values with a direct path and without a direct path. The results show that delay spread has a significant effect on the performance and training sequence length can be reduced.