An improved digital predistortion scheme for nonlinear transmitters with limited bandwidth

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-11-28 DOI:10.1016/j.dsp.2024.104874
Linshan Zhao , Kai Ying , Disheng Xiao , Jian Pang , Kai Kang
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引用次数: 0

Abstract

In modern wireless communication systems, wide signal bandwidth is the most straightforward approach to accommodate high data rates. Wide signal bandwidth, on the other hand, introduces severe challenges to the power amplifier (PA) and digital predistortion (DPD) design in both performance and cost. Conventional DPD systems usually ignore the impact of the transmit low-pass filter (Tx LPF) bandwidth and assume the transmit bandwidth is sufficiently large. In wideband signal transmissions, the bandwidth of Tx LPF can become the system bottleneck, limiting DPDs compensation effects. Existing DPD studies mostly investigate the DPD with reduced feedback bandwidth. In this paper, we study the impact of Tx LPF bandwidth on the DPD performance. A full-band error minimization DPD based on direct learning structure is proposed. The DPD coefficients are estimated by minimizing the full-band error between the input signal and PA output signal in the frequency domain. Furthermore, we propose a weighted DPD with improved performance by introducing a weighting diagonal matrix to the error function. Compared to existing solutions, the weighted DPD achieves a good trade-off between the in-band distortion compensation and out-of-band spectral regrowth suppression. Simulations and experiments validate the effectiveness of the proposed DPD schemes.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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