TY - JOUR
T1 - Digital Signal Recovery with Transmitter Nonlinear State Tracking for Satellite Communications
AU - Chen, Qingyue
AU - Li, Yunfeng
AU - Jalili, Feridoon
AU - Wang, Zhugang
AU - Jensen, Ole Kiel
AU - Pedersen, Gert Frølund
AU - Shen, Ming
PY - 2022/12/1
Y1 - 2022/12/1
N2 - This brief proposes a digital signal recovery (DSR) method to compensate the nonlinear distortion introduced by power amplifiers (PAs) under dynamic nonlinear operating states. Unlike conventional PA linearization methods that extract the nonlinearity based on the baseband I/Q PA input and output signal samples, the proposed method attempts to derive the memory polynomial (MP) model parameters based on PA operating states using a deep neural network (DNN). This method allows the receiver to achieve DSR by tracking the operating states of the PA effectively with a few telemetry data. Validation results from simulations and experiments based on a GaN PA operating at 3.5 GHz reveal that the proposed method can maintain satisfactory DSR performance in terms of adjacent channel power ratio (ACPR) and error vector magnitude (EVM) while the transmitter PA is operating with fluctuating average input/output power, supply voltage, and bias voltage. The training data size and time are further reduced by using a transfer learning (TL) approach.
AB - This brief proposes a digital signal recovery (DSR) method to compensate the nonlinear distortion introduced by power amplifiers (PAs) under dynamic nonlinear operating states. Unlike conventional PA linearization methods that extract the nonlinearity based on the baseband I/Q PA input and output signal samples, the proposed method attempts to derive the memory polynomial (MP) model parameters based on PA operating states using a deep neural network (DNN). This method allows the receiver to achieve DSR by tracking the operating states of the PA effectively with a few telemetry data. Validation results from simulations and experiments based on a GaN PA operating at 3.5 GHz reveal that the proposed method can maintain satisfactory DSR performance in terms of adjacent channel power ratio (ACPR) and error vector magnitude (EVM) while the transmitter PA is operating with fluctuating average input/output power, supply voltage, and bias voltage. The training data size and time are further reduced by using a transfer learning (TL) approach.
KW - Employee welfare
KW - Fluctuations
KW - Integrated circuit modeling
KW - Nonlinear distortion
KW - Operating states tracking
KW - Receivers
KW - Telemetry
KW - Training
KW - deep neural network
KW - digital signal recovery
KW - power amplifier
KW - satellite communications
UR - http://www.scopus.com/inward/record.url?scp=85132743781&partnerID=8YFLogxK
U2 - 10.1109/TCSII.2022.3181785
DO - 10.1109/TCSII.2022.3181785
M3 - Journal article
SN - 1549-7747
VL - 69
SP - 4774
EP - 4778
JO - I E E E Transactions on Circuits and Systems. Part 2: Express Briefs
JF - I E E E Transactions on Circuits and Systems. Part 2: Express Briefs
IS - 12
M1 - 9792424
ER -