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MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers
April 25, 2024, 7:42 p.m. | Yizhuo Wu, Ang Li, Mohammadreza Beikmirza, Gagan Deep Singh, Qinyu Chen, Leo C. N. de Vreede, Morteza Alavi, Chang Gao
cs.LG updates on arXiv.org arxiv.org
Abstract: Digital Pre-Distortion (DPD) enhances signal quality in wideband RF power amplifiers (PAs). As signal bandwidths expand in modern radio systems, DPD's energy consumption increasingly impacts overall system efficiency. Deep Neural Networks (DNNs) offer promising advancements in DPD, yet their high complexity hinders their practical deployment. This paper introduces open-source mixed-precision (MP) neural networks that employ quantized low-precision fixed-point parameters for energy-efficient DPD. This approach reduces computational complexity and memory footprint, thereby lowering power consumption without …
arxiv complexity cs.ai cs.cv cs.lg digital dpd eess.sp energy low mixed mixed-precision networks neural networks power precision type
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