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Multi-tone, Multi-port, and Dynamic Memory Enhancements to PHD Nonlinear Behavioral Models from Large-signal Measurements and Simulations

机译:从大信号测量和仿真到PHD非线性行为模型的多音,多端口和动态内存增强

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The PHD nonlinear behavioral model is extended to handle multiple large tones at an arbitrary number of ports, and enhanced for dynamic long-term memory. New capabilities are exemplified by an amplifier model, derived from large-signal network analyzer (LSNA) data, valid for arbitrary impedance environments, and a model of a 50GHz integrated mixer, including leakage terms and IF mismatch dependence. Dynamic memory is demonstrated by an HBT amplifier model identified from up-converted band-limited noise excitations. The models are validated with independent LSNA component data or, for simulation-based models, with the corresponding circuit models.
机译:PHD非线性行为模型已扩展为可以在任意数量的端口上处理多个大音调,并为动态长期记忆进行了增强。新功能以放大器模型为例,该模型源自大信号网络分析仪(LSNA)数据,适用于任意阻抗环境,并具有5​​0GHz集成混频器模型,其中包括泄漏项和IF不匹配依赖性。通过从上变频的带限噪声激励中识别出的HBT放大器模型演示了动态内存。这些模型通过独立的LSNA组件数据进行验证,或者对于基于仿真的模型,通过相应的电路模型进行验证。

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