DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion

IF 3.4 0 ENGINEERING, ELECTRICAL & ELECTRONIC
Yizhuo Wu;Yi Zhu;Kun Qian;Qinyu Chen;Anding Zhu;John Gajadharsing;Leo C. N. de Vreede;Chang Gao
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引用次数: 0

Abstract

Digital predistortion (DPD) is a popular technique to enhance signal quality in wideband radio frequency (RF) power amplifiers (PAs). With increasing bandwidth and data rates, DPD faces significant energy consumption challenges during deployment, contrasting with its efficiency goals. State-of-the-art DPD models rely on recurrent neural networks (RNNs), whose computational complexity hinders system efficiency. This letter introduces DeltaDPD, exploring the dynamic temporal sparsity of input signals and neuronal hidden states in RNNs for energy-efficient DPD, reducing arithmetic operations and memory accesses while preserving satisfactory linearization performance. Applying a TM3.1a 200 MHz-BW 256-QAM OFDM signal to a 3.5-GHz GaN Doherty RF PA, DeltaDPD achieves −50.03 dBc in adjacent channel power ratio (ACPR), −37.22dB in normalized mean square error (NMSE) and −38.52 dB in error vector magnitude (EVM) with 52% temporal sparsity, leading to a $1.8\times $ reduction in estimated inference power. The DeltaDPD code is available at https://www.opendpd.com.
利用递归神经网络的动态时间稀疏性实现高能效宽带数字预失真
数字预失真(DPD)技术是宽带射频功率放大器中提高信号质量的一种常用技术。随着带宽和数据速率的增加,DPD在部署过程中面临着巨大的能耗挑战,这与其效率目标形成了鲜明对比。现有的DPD模型依赖于递归神经网络(rnn),其计算复杂性阻碍了系统的效率。本文介绍了DeltaDPD,探索了rnn中输入信号的动态时间稀疏性和神经元隐藏状态,用于节能DPD,减少了算术运算和内存访问,同时保持了令人满意的线性化性能。将TM3.1a 200 MHz-BW 256-QAM OFDM信号应用于3.5 ghz GaN Doherty射频放大器,DeltaDPD实现了相邻信道功率比(ACPR) - 50.03 dBc,归一化均方误差(NMSE) - 37.22dB和误差矢量幅度(EVM) - 38.52 dB,时间稀疏度为52%,导致估计推断功率降低1.8倍。DeltaDPD代码可从https://www.opendpd.com获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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