Max-min fair multigroup multicast in an IRS-aided MISO system
TL;DR
Simulation results demonstrate the guaranteed convergence and the superiority of the proposed scheme in improving the minimum weighted SINR at the users, as well as the contribution of the IRS in reducing the transmit power consumption and the number of active transmit antennas at the AP.
Abstract
IRS has received much attention recently and is envisioned as a revolutionary technology for 6G communication networks. In this paper, we consider a multicast traffic pattern in an IRS-aided single-cell MISO communication system, where an IRS is deployed to assist a multi-antenna AP in transmitting independent data streams to each group of multiple single-antenna users. We formulate and solve a new MMF problem by jointly optimizing the beamforming matrix at the AP and the phase shift matrix at the IRS, subject to both the power constraint and the unit-modulus constraints on the phase shifts. To tackle this highly nonconvex fractional problem, efficient algorithms are proposed based on GFP and AO. In each alternating step, a closed-form suboptimal solution for the transmit beamforming matrix is derived using SCA and LDD. For the phase shift optimization of the IRS, a penalty mechanism is introduced to efficiently handle the nonconvex unit-modulus constraints. Simulation results demonstrate the guaranteed convergence and the superiority of the proposed scheme in improving the minimum weighted SINR at the users, as well as the contribution of the IRS in reducing the transmit power consumption and the number of active transmit antennas at the AP.