Neural-Network-Based Digital Predistortion for Active Antenna Arrays under Load Modulation
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||4|
|Journal||IEEE Microwave and Wireless Components Letters|
|Publication status||Published - 1 Aug 2020|
|Publication type||A1 Journal article-refereed|
In this letter, we propose an efficient solution to linearize mmWave active antenna array transmitters that suffer from beam-dependent load modulation. We consider a dense neural network that is capable of modeling the correlation between the nonlinear distortion characteristics among different beams. This allows providing consistently good linearization regardless of the beamforming direction, thus avoiding the necessity of executing continuous digital predistortion parameter learning. The proposed solution is validated, conforming to 5G new radio transmit signal quality requirements, with extensive over-the-air RF measurements utilizing a state-of-the-art 64-element active antenna array operating at 28-GHz carrier frequency.
- 5G new radio (NR), digital predistortion (DPD), load modulation, mmWave, neural networks (NNs), nonlinear distortion