Abstract
Analog beamforming has been considered an attractive technology for future single carrier millimeter-wave multiple-input multiple-output (MIMO) systems because of the high cost and huge power consumption of mixed-signal devices. Most conventional studies have focused on joint base station and user equipment (BS-UE) analog beamforming with the objective of improving the average signal to noise ratio performance before the baseband equalization. In contrast, this paper aims to optimize the BS-UE analog beamforming vectors in the sense of minimizing the mean square error of the baseband equalized signal. Considering practical implementation requirement, we combine the gradient descent (GD) method and the iterative antenna training (IAT) technique, and propose an iterative local GD (ILGD) algorithm. We analyze the convergence property, bit error rate (BER) performance, training overhead, and computational complexity of the ILGD algorithm. Simulation results show that the proposed ILGD algorithm can achieve a gain of more than 2 dB at a BER of $10^{-4}$ over the conventional IAT algorithm with the same training overhead.
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