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• W3045539012 abstract "This paper introduces the measurement/modeling of a nonlinear RF power amplifier (PA) model extraction of spectral overlaps and peak-to-average power ratio (PAPR). The modeling consists of a weighted cubic-spline basis approach that preserves a relationship by its generic asymptotic properties under adequate PAPR regime to estimate signaling conditions. To jointly provide a data-driven model a field-programmable gate array (FPGA) testbed is proposed, in which a precomputed approach with deterministic signals is used to perform: (i) parameter estimation; (ii) adequate PAPR levels; (iii) reinforcement of sparsity data with extrapolation fitting; and (iv) FPGA implementation. Moreover, a technique for parameter identification is introduced to provide insights of a digital predistortion (DPD) PA model extraction with cubic-spline to efficiently improve the linearization performance. A theoretical analysis that states on the benefits from coefficients precomputation to preserve multiple-input multiple-output (MIMO) relationship with antenna selection technique estimation, is also proposed. Also, a Cholesky FPGA algorithm implementation to matrix inversion is validated, which aims to show the good numerical and computational complexity for up to $64times 64$ MIMO arrays. Experimental results prove a good accuracy and close agreement between the modeling and estimation yielding a reliable model with a little overfitting." @default.
• W3045539012 created "2020-08-03" @default.
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• W3045539012 date "2020-01-01" @default.
• W3045539012 modified "2023-10-02" @default.
• W3045539012 title "RF-PA Modeling of PAPR: A Precomputed Approach to Reinforce Spectral Efficiency" @default.
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• W3045539012 doi "https://doi.org/10.1109/access.2020.3012610" @default.
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