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Calibration of Phase Shifter Network for Hybrid Beamforming in mmWave Massive MIMO Systems

机译：mmWave大规模MIMO系统中用于混合波束成形的移相器网络校准

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For the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid beamforming has been proposed to reap the great gain of the large number of antennas with a limited number of radio frequency (RF) chains. The hybrid beamforming relies on a phase shifter network (PSN) in the RF domain to steer the signal power along the desired direction (or subspace). However, the RF circuits connecting the antennas and the RF chains can introduce distinct phase deviations, which need to be calibrated for efficient hybrid beamforming designs. This paper develops a novel approach to the estimation and calibration of the PSN in mmWave massive MIMO communication systems. To this end, we formulate the calibration problem as an optimization program with the constant modulus constraint. An efficient iterative algorithm is then proposed to estimate the phase deviations to be calibrated. We also derive the Cramer-Rao lower bound (CRLB) of the phase estimates. The numerical results validate the efficiency of our approach by showing that the algorithm yields estimates whose mean squared errors (MSE) are close to the CRLB.
机译：对于毫米波（mmWave）大规模多输入多输出（MIMO）系统，已经提出了混合波束成形，以利用有限数量的射频（RF）链获得大量天线的巨大收益。混合波束成形依赖于RF域中的移相器网络（PSN）来沿所需方向（或子空间）操纵信号功率。但是，连接天线和RF链的RF电路可能会引入明显的相位偏差，这需要针对有效的混合波束成形设计进行校准。本文开发了一种新颖的方法，用于mmWave大规模MIMO通信系统中PSN的估计和校准。为此，我们将校准问题公式化为具有恒定模量约束的优化程序。然后提出一种有效的迭代算法来估计要校准的相位偏差。我们还推导了相位估计的Cramer-Rao下界（CRLB）。数值结果证明了该算法得出的均方误差（MSE）接近CRLB的估计值，验证了我们方法的有效性。

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