In this paper, we investigate multicell-coordinated beamforming for large-scale multiple-input multipleoutput (MIMO) orthogonal frequency-division multiplexing (OFDM) communications with low-resolution data converters. In particular, we seek to minimize the total transmit power of the network under received signal-to-quantization-plus-interference-and-noise ratio constraints while minimizing per-antenna transmit power. Our primary contributions are (1) formulating the quantized downlink (DL) OFDM antenna power minimax problem and deriving its associated dual problem, (2) showing strong duality and interpreting the dual as a virtual quantized uplink (UL) OFDM problem, and (3) developing an iterative minimax algorithm to identify a feasible solution based on the dual problem with performance validation through simulations. Specifically, the dual problem requires joint optimization of virtual UL transmit power and noise covariance matrices. To solve the problem, we first derive the optimal dual solution of the UL problem for given noise covariance matrices. Then, we use the solution to compute the associated DL beamformer. Subsequently, using the DL beamformer we update the UL noise covariance matrices via subgradient projection. Finally, we propose an iterative algorithm by repeating the steps for optimizing DL beamformers. Simulations validate the proposed algorithm in terms of the maximum antenna transmit power and peak-to-average-power ratio.

翻译：在本文中,我们调查了与低分辨率数据转换器进行大规模多输出多输出比率(MIMO)或交点频率多维转换(OFDM)通信的多细胞协调组合,我们特别力求最大限度地减少在接收信号到量化+互换和噪音比限制下,网络在接收信号到量化+互换和互换比例限制下的总传输能力,同时尽量减少单安纳传输能力。我们的主要贡献是:(1) 开发DM天线功率微减问题(DL)的量化下链(DL),并引出与此相关的双重问题;(2) 显示强大的双重性和将双重性解释为DL最高定量的DMDRRV(O)的虚拟升级链接(UL),以及(3) 开发一个基于通过模拟验证业绩的双重问题的迭代小型算法,找出可行的解决办法。具体地说,需要联合优化虚拟UL传输电量和噪音变异矩阵。为了解决问题,我们首先为给噪音变异矩阵的UL(UL)问题找到最佳的双重解决办法。然后,我们用这个解决办法将相关的DL(UL)级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平级平基。