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GRAB: Joint Adaptive Grouping and Beamforming for Multi-Group Multicast with Massive MIMO

We consider the problem of downlink multicast transmission of user data in massive MIMO systems. Due to the nature of multicast transmission, the common data rate in a multicast group is constrained by that of the user with the worst Signal-to-Noise-Ratio (SNR). As a consequence, serving a large number of users in a single multicast group might degrade the system performance. To overcome this drawback, utilizing spatial degrees of freedom offered by a large number of transmit antennas, we propose to dynamically divide the set of serving users into multiple multicast groups and jointly design the user grouping pattern and co-channel beamforming vectors of these groups. Given the NP-hardness of the considered problem, we decompose it into a multi-group multicast beamforming subproblem and a user grouping subproblem. We proposed several low-complexity methods to iteratively solve these subproblems in order to obtain a suboptimal solution to the original problem. Simulation results show that our proposed GRouping And Beamforming (GRAB) scheme achieve significantly higher average sum-rate performance compared to that of the existing multicast schemes.