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Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters
  • Yunseong Cho ,
  • Jinseok Choi ,
  • Brian Evans
Yunseong Cho
The University of Texas at Austin

Corresponding Author:[email protected]

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Jinseok Choi
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Brian Evans
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In this paper, we investigate multicell-coordinated beamforming for large-scale multiple-input multiple-output (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.
2023Published in IEEE Transactions on Communications on pages 1-1. 10.1109/TCOMM.2023.3326206