Design of RF-Input Sequential LMBA Using PSO Algorithm With Improved Linear Self-Adaptive Hyper-Parameters
Zhiming Fan, Zefang Hao, Jiajun Huang, Jialin Cai
Abstract
In this article, an improved particle swarm optimization (PSO) algorithm is proposed for the realization of a wideband RF-input sequential load modulation balanced amplifier (SLMBA). A description of the SLMBA topology as well as the proposed improved PSO method with linear self-adaptive hyperparameter (Improved PSO-LSH) is provided. The design process and optimization procedure are described in detail. Performance of the SLMBA optimized by the proposed method is presented, and compared with the initial design, the ADS built-in optimizer, the PSO method with fixed hyperparameters (PSO-FH), and the PSO-LSH method. According to the results, the proposed optimization algorithm can make both the saturation and back-off impedance matching trajectories closer to the optimal load trajectories, and better performance can be obtained. Based on the measured results, the SLMBA designed using the proposed method achieves a saturation drain efficiency (DE) between 62.4% and 71.6%, and a DE of 50.2%–59.9% in the 10-dB output back-off (OBO) state, with an output power of 42–43.5 dBm from 1.7 to 2.2 GHz. In addition, digital predistortion (DPD) is implemented to linearize the designed power amplifier (PA) with a 5G new radio (NR) signal. The adjacent channel power ratios (ACPRs) can be maintained at less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 53 dBc after DPD.