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We consider the problem of coordinated multi- cell downlink beamforming inmassive multiple input multiple output (MIMO) systems consisting of N cells, Ntantennas per base station (BS) and K user terminals (UTs) per cell.Specifically, we formulate a multi-cell beamforming algorithm for massive MIMOsystems which requires limited amount of information exchange between the BSs.The design objective is to minimize the aggregate transmit power across all theBSs subject to satisfying the user signal to interference noise ratio (SINR)constraints. The algorithm requires the BSs to exchange parameters which can becomputed solely based on the channel statistics rather than the instantaneousCSI. We make use of tools from random matrix theory to formulate thedecentralized algorithm. We also characterize a lower bound on the set oftarget SINR values for which the decentralized multi-cell beamforming algorithmis feasible. We further show that the performance of our algorithmasymptotically matches the performance of the centralized algorithm with fullCSI sharing. While the original result focuses on minimizing the aggregatetransmit power across all the BSs, we formulate a heuristic extension of thisalgorithm to incorporate a practical constraint in multi-cell systems, namelythe individual BS transmit power constraints. Finally, we investigate theimpact of imperfect CSI and pilot contamination effect on the performance ofthe decentralized algorithm, and propose a heuristic extension of the algorithmto accommodate these issues. Simulation results illustrate that our algorithmclosely satisfies the target SINR constraints and achieves minimum power in theregime of massive MIMO systems. In addition, it also provides substantial powersavings as compared to zero-forcing beamforming when the number of antennas perBS is of the same orders of magnitude as the number of UTs per cell.

Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach