An optimal balance between high efficiency and high linearity is one of the main performance metrics of modern base stations to handle the deep compression of the power amplifier (PA) module. In principle, the plethora of amplitude-to-amplitude (AM-AM) and amplitude-to-phase (AM-PM) distortions are issues worth exploring. Therefore, this paper presents a linearized harmonic-tuned PA that operates at 2.4 GHz. The presented PA utilizes a compact input matching network (IMN) and output MN (OMN) with shuntconnected tunable resonant circuits augmented by the stabilization network, whose presence greatly reduces the transistor parasitics and high-order effects.Hence, a joint embodiment of each other gives optimal fundamental impedance matching alongside harmonic terminations and precise AM/PM waveform properties. The measurement results have shown that the fabricated PA exhibits peak saturated output power of 40.9 dBm and peak drain efficiency (DE) of 67%. In addition, the AM-AM and AM-PM curves under continuous-wave excitation yield a gain flatness of 0.5 dB over a 37.5 dBm power range and ±3.5°phase distortion, respectively, when the input power level is swept up to the saturation level of 40 dBm. When driven with a 5 MHz 64-QAM OFDM signal and 6.9 dB power back-off, the manufactured PA meets the adjacent channel leakage ratio specification of −30 dBc at an average output power of 34.5 dBm.
To cope with the degradation of digital predistortion when bandwidth changes, a bandwidth adaptive behavioral model with dynamic structures for radio frequency (RF) power amplifiers (PAs) is proposed in this article. By applying the nonlinear post-compensation technology, PA's nonlinearity is decomposed and analyzed. Short-time Fourier transform and transfer functions are adopted to study the influence of bandwidth variation on the linear memory effect. Based on PA's memory effect analysis, a bandwidth adaptive model with dynamic structures and a coefficient update scheme with low complexity are proposed. Experiments carried on PAs show that the proposed model achieves similar linearization performance as other conventional models. Simultaneously, the proposed model has fewer coefficients and a reduction in the coefficient update of at least 50%.
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