Abstract
Recently, intelligent reflecting surface (IRS) has received considerable attention and is seen as a promising technology for 6 G communications. This correspondence studies the IRS-aided multiuser multi-input single-output (MISO) downlink multicast beamforming. Depart from the conventional (rank-one) beamforming, the Alamouti-aided <i>rank-two</i> beamforming is employed at the base station (BS), and a joint optimization of the rank-two beamforming and the IRS phase shift is formulated to minimize the transmit power with guaranteed signal-to-noise ratio at the users. This power minimization problem is nonconvex. To tackle it, we custom-derive a proximal distance algorithm (PDA) with generalized power method (GPM) to iteratively optimize the beamformer and the phase shift. Both PDA and GPM can be performed very efficiently than the semidefinite relaxation method. Simulation results demonstrate that the rank-two beamforming attains much lower porwer than the rank-one beamforming with reduced computation complexity.
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