Digital Signal Recovery with Transmitter Nonlinear State Tracking for Satellite Communications

Qingyue Chen, Yunfeng Li, Feridoon Jalili, Zhugang Wang, Ole Kiel Jensen, Gert Frølund Pedersen, Ming Shen

Research output: Contribution to journalJournal articleResearchpeer-review

19 Downloads (Pure)


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.

Original languageEnglish
Article number9792424
JournalI E E E Transactions on Circuits and Systems. Part 2: Express Briefs
Issue number12
Pages (from-to)4774-4778
Number of pages5
Publication statusPublished - 1 Dec 2022


  • Employee welfare
  • Fluctuations
  • Integrated circuit modeling
  • Nonlinear distortion
  • Operating states tracking
  • Receivers
  • Telemetry
  • Training
  • deep neural network
  • digital signal recovery
  • power amplifier
  • satellite communications


Dive into the research topics of 'Digital Signal Recovery with Transmitter Nonlinear State Tracking for Satellite Communications'. Together they form a unique fingerprint.

Cite this