Measurement and nonlinear behavioral modeling of the dynamic bias current in an LTE‐A <scp>C</scp>hireix PA

Galaviz-Aguilar, José Alejandro; Chang, Hsin-Yu; Martinez-Rodriguez, Francisco J.; Roblin, Patrick; Pérez, José Cruz Núñez

doi:10.1002/mop.30817

Cited by 4 publications

(1 citation statement)

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“…Under this assumption, a surrogate model is derived from the normalized output P out /P out,max = |y| 2 . A similar model is reported in [51] to account for the power dissipation modeling which is able to optimize the dynamic efficiency. For the PA power consumed at each separated branch, and the average power consumed per user and the total system consumed by the PAs, but such dominated by the power consumed in a large regime under an efficiency given in (25) and linear system driving, targeted by: |y(n)| = P out,avg = Loss + P out,avg P max = P out,avg + P AP R (…”

Section: Nonlinear Power Amplifiers Impact To Rf Chains Large Arraymentioning

confidence: 99%

RF-PA Modeling of PAPR: A Precomputed Approach to Reinforce Spectral Efficiency

2020

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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 fieldprogrammable 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 cubicspline 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 64 × 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.

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Section: Nonlinear Power Amplifiers Impact To Rf Chains Large Arraymentioning

confidence: 99%