Adaptive Beamforming for Non-Line-of-Sight IRS-Assisted Communications without CSI

Channel acquisition is a major bottleneck in fully exploiting the potential of intelligent reflecting surfaces (IRSs) to improve the wireless environment. In order to bypass such difficulty, an alternative is to optimize IRS based on the received signal statistics rather than channel state information (CSI), namely blind beamforming. The two recent methods, RFocus and conditional sample mean (CSM), fall into this category, both of which have been shown highly effective in practice. Nevertheless, we find a subtle drawback with the existing blind beamforming methods that they may not work well for the non-line-of-sight (NLoS) case for two reasons. First, many more signal samples are needed when the direct propagation diminishes. Second, if the direct propagation is completely blocked then the existing blind beamforming methods cannot work whatsoever. To address this issue, we propose an adaptive strategy for blind beamforming, which guarantees an approximation ratio of the global optimum. Field tests and simulations show that the proposed blind beamforming method is much more suited for NLoS environment than the existing ones.