Z
Zhijiang Dai
Researcher at Chongqing University
Publications - 54
Citations - 808
Zhijiang Dai is an academic researcher from Chongqing University. The author has contributed to research in topics: Amplifier & Computer science. The author has an hindex of 13, co-authored 32 publications receiving 540 citations. Previous affiliations of Zhijiang Dai include University of Electronic Science and Technology of China.
Papers
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Journal ArticleDOI
A Post-Matching Doherty Power Amplifier Employing Low-Order Impedance Inverters for Broadband Applications
TL;DR: In this paper, a modified Doherty Power amplifier was designed and fabricated based on commercial GaN HEMT devices to validate the broadband characteristics of this configuration, and the measured maximum output power ranges from 44.9 to 46.3 dBm in the designed band.
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A New Distributed Parameter Broadband Matching Method for Power Amplifier via Real Frequency Technique
TL;DR: In this paper, a general matching method is presented for broadband power amplifier (PA) design, and a novel cost function is proposed for the RFT, which could straightforwardly describe PA optimal impedance along with frequency change.
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Design of a Post-Matching Asymmetric Doherty Power Amplifier for Broadband Applications
TL;DR: In this article, a broadband asymmetric Doherty power amplifier (ADPA) with an 800 MHz (41% fractional) bandwidth is presented, where post-matching structure and low-order impedance transformation networks (ITN) are employed to achieve the broadband performance.
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Analysis and Design of Highly Efficient Wideband RF-Input Sequential Load Modulated Balanced Power Amplifier
TL;DR: It is illustrated that the sequential operation greatly extends the high-efficiency power range and enables the proposed SLMBA to achieve high back-off efficiency across a wide bandwidth.
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Digital Predistortion for Power Amplifier Based on Sparse Bayesian Learning
TL;DR: A sparse-Bayesian-learning algorithm is applied to estimate the coefficients of the power amplifier behavioral models and inverse models from the view of probability, and with this sparse learning method, the needed number of samplings can be reduced significantly.