No abstract

No abstract

In this work, we propose an opportunistic beamforming strategy, which enables beam selection through randomrotations of an intelligent reflecting surface (IRS). To boost performance over a time slot, the proposed scheme splits the training period into multiple mini-slots. In each mini-slot, the access point generates different sets of orthonormal beamforming vectors and the IRS employs random-rotations. We provide an analytical framework for the sum-rate capacity and it is shown that a trade-off between the sum-rate capacity and the length of the training period exists due to the time constraint on the communication process. Based on this, we also derive the optimal number of the training mini-slots. The proposed lowcomplexity scheme outperforms conventional counterparts (single training slot) and approximates the performance of conventional beamforming (with channel state information) even for small number of users. Finally, by utilizing extreme value theory tools, we analyze the system's performance under an asymptotic scenario, where the number of the users significantly increases.