A Reflection-Aware Unified Modeling and Linearization Approach for Power Amplifier Under Mismatch and Mutual Coupling

Dhar, Sagar K.; Abdelhafiz, Abubaker; Aziz, Mohsin; Helaoui, Mohamed; Ghannouchi, Fadhel M.

doi:10.1109/tmtt.2018.2859959

Cited by 22 publications

(25 citation statements)

References 34 publications

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“…Rather, it is necessary to extract the models across multiple load terminations to capture the global behavior. Dhar etal,in [17], show that these load-dependent models provide a significant improvement in prediction accuracy. In particular, the interaction (cross) terms taken into consideration in the COMP model admits a large performance increase above that available from the MP model.…”

Section: B Load-dependent Mp and Comp Modelsmentioning

confidence: 99%

“…During the past decade, different DIDO behavioral modeling methods for dynamic PAs including mismatch effects have been provided [12]- [17]. Many such models are based on Volterra or polynomial structures.…”

Section: Existing Nonlinear Behavioral Models Of Pas Including Lomentioning

confidence: 99%

“…In [17], a load-dependent dynamic modeling technique is proposed, and applied to MP [see (2)] and COMP [see (3)] models. The expression of these two models are described below,…”

Section: B Load-dependent Mp and Comp Modelsmentioning

confidence: 99%

“…The model described by (16) represents a static system, without memory. To account for devices with memory we adjust the model according tõ (17) where M refers the memory depth of the model. This expression is exactly the same as the DVR model function (10) in [28].…”

Section: ) Cpl Technique For Conventional Siso Modelmentioning

confidence: 99%

“…In [16], both dc and RF behavior, with mutual coupling, is captured in the model in order to allow PA efficiency determination. In [17], a reflection-aware modeling technique is proposed for PAs under both mismatch and mutual coupling conditions, with improved prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Behavioral Model of RF PAs Including Load Mismatch Effects Based on Canonical Section-wise Piecewise-Linear Function

Cai

King

2020

IEEE Access

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A new, dynamic, double-input, double-output (DIDO), wideband, dynamic, complex envelope, behavioral model for power amplifiers (PAs), accounting for load mismatch effects, is proposed and validated in this paper. The model is constructed based on two-dimensional, dynamic, canonical section-wise piecewise-linear functions (2-D DCSWPL). Compared with existing DIDO behavioral modeling methods, which are almost all exclusively based on polynomial functions, the proposed model permits a vastly extended modeling space for PAs under load mismatch conditions. Experimental examples are given to validate the proposed modeling technique with a 15 W GaN PA under a wide range of output mismatch conditions, driven by a 5 MHz LTE signal. The proposed model shows an average accuracy improvement of 2 dB when compared with the more traditional DIDO memory polynomial (MP) models. In addition, the load reflection-dependent dynamic 2-D CSWPL modeling technique for PAs under mismatch condition is also provided. Compared with load-dependent MP, load-dependent crossover MP (COMP) and load-dependent DIDO MP models, the proposed model shows superior accuracy. Thus, the proposed modeling technique is an excellent choice for the modelling of PA systems that are subject to load mismatch effects. INDEX TERMS Canonical section-wise piecewise-linear Function, dynamic modeling, load mismatch effects, nonlinear behavioral Modeling, RF PAs.

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Section: B Load-dependent Mp and Comp Modelsmentioning

confidence: 99%

Section: Existing Nonlinear Behavioral Models Of Pas Including Lomentioning

confidence: 99%

“…In [17], a load-dependent dynamic modeling technique is proposed, and applied to MP [see (2)] and COMP [see (3)] models. The expression of these two models are described below,…”

Section: B Load-dependent Mp and Comp Modelsmentioning

confidence: 99%

Section: ) Cpl Technique For Conventional Siso Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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