Over-The-Air Calibration of A Phase Shifter Network

Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for its low cost and analog signal processing capability. In practice, the phase shifters may be subject to phase deviations, which needs to be properly estimated and calibrated. This paper proposes a novel o...

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Bibliographic Details
Published in2022 IEEE/CIC International Conference on Communications in China (ICCC) pp. 500 - 505
Main Authors Zhang, Wei, Jiang, Yi
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.08.2022
Subjects
back propagation algorithm
Backpropagation
Cramer-Rao bounds
Gears
MIMO
phased array calibration
Phased arrays
quasi-neural network
Signal processing algorithms
Simulation
Training
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Summary:Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for its low cost and analog signal processing capability. In practice, the phase shifters may be subject to phase deviations, which needs to be properly estimated and calibrated. This paper proposes a novel over-the-air (OTA) approach to estimate the deviations of the phase shifters at each gear. We formulate the PSN calibration model by the so-termed quasi-neural network (quasi-NN). In training the quasi-NN using the back propagation (BP) algorithm, the phase deviations are automatically estimated. The simulation results verify the effectiveness of the proposed algorithm by showing that the root mean square errors (RMSEs) of the phase estimates are close to the Cramer Rao Bounds (CRBs).
DOI:10.1109/ICCC55456.2022.9880831