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

翻译：针对采用低分辨率数据转换器及每天线级约束的大规模多输入多输出正交频分复用（OFDM）系统，提出了一种多小区协调波束赋形解决方案。为更贴近实际部署场景，我们致力于在接收信号质量约束下，找出能使每个基站发射天线阵列最大功率最小化且同时降低每天线发射功率的下行链路波束赋形器。我们证明了下行原始问题与其可解的拉格朗日对偶问题之间存在强对偶性，该对偶问题可被解释为具有可调噪声协方差矩阵的虚拟上行链路问题。对于一组固定的噪声协方差矩阵，我们提出可利用虚拟上行链路解高效计算下行波束赋形器，随后通过关联的次梯度方法更新噪声协方差矩阵。本文的主要贡献包括：(1) 构建量化下行OFDM天线功率最小最大问题并推导其关联对偶问题；(2) 证明强对偶性并将对偶问题解释为虚拟量化上行OFDM问题；(3) 基于对偶问题开发迭代最小最大算法。仿真实验从最大天线发射功率和峰均功率比两方面验证了所提算法的有效性。