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A multicell-coordinated beamforming solution for massive multiple-inputmultiple-output orthogonal frequency-division multiplexing (OFDM) systems ispresented when employing low-resolution data converters and per-antenna levelconstraints. For a more realistic deployment, we aim to find the downlink (DL)beamformer that minimizes the maximum power on transmit antenna array of eachbasestation under received signal quality constraints while minimizingper-antenna transmit power. We show that strong duality holds between theprimal DL formulation and its manageable Lagrangian dual problem which can beinterpreted as the virtual uplink (UL) problem with adjustable noise covariancematrices. For a fixed set of noise covariance matrices, we claim that thevirtual UL solution is effectively used to compute the DL beamformer and noisecovariance matrices can be subsequently updated with an associated subgradient.Our primary contributions are then (1) formulating the quantized DL OFDMantenna power minimax problem and deriving its associated dual problem, (2)showing strong duality and interpreting the dual as a virtual quantized UL OFDMproblem, and (3) developing an iterative minimax algorithm based on the dualproblem. Simulations validate the proposed algorithm in terms of the maximumantenna transmit power and peak-to-average-power ratio.

Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters