Now showing 1 - 2 of 2
  • Publication
    Data-Clustering-Assisted Digital Predistortion for 5G Millimeter-Wave Beamforming Transmitters With Multiple Dynamic Configurations
    Motivated by data science, in this article, a data-clustering-assisted digital predistortion (DPD) is proposed to linearize millimeter-wave (mmWave) beamforming transmitters with multiple dynamic configurations. Based on the data analysis, similar transmitter states with different configurations can be clustered, resulting in a significant reduction of linearization states. Model complexity within the cluster can be further reduced by utilizing a penalty factor method. To validate the proposed concept, experiments were carried out on a 16-channel mmWave beamforming transmitter with configurable beam angle, operating frequency, and input power. Total 216 transmitter states can be reduced to 8, 20, and 40 states with unequal optimal model parameters for each state, without losing much performance. The proposed method can be extended to the scenario where a large scale of dynamic states can occur in complex transmitter structures in future wireless systems.
      287Scopus© Citations 7
  • Publication
    Pattern Sensing Based Digital Predistortion of RF Power Amplifiers under Dynamical Signal Transmission
    In this paper, a pattern sensing based digital predistortion (DPD) technique for radio frequency (RF) power amplifiers (PAs) under dynamical signal transmission is proposed. Unlike conventional methods where real time re-calibration is required, this approach utilizes a low resolution amplitude-modulation to amplitude-modulation (AM/AM) pattern to sense PA characteristics and then quickly select proper DPD coefficients to linearize the PA. Experimental results show that the proposed method can provide an efficient a nd effective w ay to deal with complex dynamic signal transmission scenarios and maintain very good linearization performance, which is very suitable for future 5G applications.
      119Scopus© Citations 2