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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2022 IEEE/CIC International Conference on Communications in China (ICCC) s. 500 - 505
Hlavní autoři: Zhang, Wei, Jiang, Yi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 11.08.2022
Témata:
back propagation algorithm
Backpropagation
Cramer-Rao bounds
Gears
MIMO
phased array calibration
Phased arrays
quasi-neural network
Signal processing algorithms
Simulation
Training
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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