Blind Beamforming for Intelligent Reflecting Surface in Fading Channels without CSI

This paper discusses how to optimize the phase shifts of intelligent reflecting surface (IRS) to combat channel fading without any channel state information (CSI), namely blind beamforming. Differing from most previous works based on a two-stage paradigm of first estimating channels and then optimizing phase shifts, our approach is completely data-driven, only requiring a dataset of the received signal power at the user terminal. Thus, our method does not incur extra overhead costs for channel estimation, and does not entail collaboration from service provider, either. The main idea is to choose phase shifts at random and use the corresponding conditional sample mean of the received signal power to extract the main features of the wireless environment. This blind beamforming approach guarantees an $N^2$ boost of signal-to-noise ratio (SNR), where $N$ is the number of reflective elements (REs) of IRS, regardless of whether the direct channel is line-of-sight (LoS) or not. Moreover, blind beamforming is extended to a double-IRS system with provable performance. Finally, prototype tests show that the proposed blind beamforming method can be readily incorporated into the existing communication systems in the real world; simulation tests further show that it works for a variety of fading channel models.

Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information

Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI. Blind beamforming only requires measuring the received signal power at the user terminal for a sequence of randomly generated phase shifts across all IRSs. The main idea is to extract the key statistical quantity for beamforming by exploring only a small portion of the whole solution space of phase shifts. We show that blind beamforming guarantees a signal-to-noise ratio (SNR) boost of Theta(N^{2L}) under certain conditions, where L is the number of IRSs and N is the number of reflecting elements per IRS. The above result significantly improves upon the state of the art in the area of multi-IRS assisted communication. Moreover, blind beamforming is justified via field tests and simulations.