OwnDPDLab：用于宽带DPD算法基准测试的灵活开源测试平台
OwnDPDLab: A Flexible Open-Source Testbed for Wideband DPD Algorithm Benchmarking
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中文总结 AI 辅助
针对5G及以后标准中DPD算法需在增加信号带宽下运行，现有测试硬件成本高且缺灵活性的问题，OwnDPDLab提供开源系统，基于RFSoC 4x2实现多参数控制，经实验展示其能力，用DPD取得误差和邻道泄漏比改善。
中文摘要 AI 辅助
5G及5G之后的标准要求数字预失真（DPD）算法在增加的信号带宽上运行。宽带实验室测试硬件成本高昂，且现有公开解决方案缺乏灵活性。OwnDPDLab提供了一个高度灵活、经济实惠、开源且可公开访问的系统。它基于RFSoC 4x2，支持在高达1GHz的信号带宽下对中心频率、采样模式、输出功率和输入衰减进行全面控制。通过在第一和第二奈奎斯特区使用记忆多项式和增强实值时延神经网络，用196.608MHz正交频分复用（OFDM）信号（256-QAM调制）对实验室功率放大器进行线性化，展示了该系统的能力。使用DPD时，系统实现了高达23dB的归一化均方误差改善和高达11dB的邻道泄漏比改善。
英文摘要
5G and Beyond-5G standards require digital predistortion (DPD) algorithms to operate on increased signal bandwidths. Wideband laboratory test hardware is cost-intensive, and openly available solutions lack flexibility. The OwnDPDLab provides a highly flexible, affordable, open-source, and openly accessible system. It is based on the RFSoC 4x2 and supports full control of center frequency, sampling mode, output power, and input attenuation at a signal bandwidth of up to 1 GHz. The system's capability is demonstrated by linearizing a laboratory power amplifier using a 196.608 MHz orthogonal frequency division multiplexing (OFDM) signal with 256-QAM modulation using both a memory polynomial and an augmented real-valued time-delay neural network in the first and second Nyquist zone. The system achieves a normalized mean squared error improvement of up to 23 dB and an adjacent channel leakage ratio improvement of up to 11 dB, using DPD.
