This paper presents a rigorous analytic approach for the prediction of the in-band and out-of-band intermodulation distortion of fifth-order memoryless nonlinear RF circuits/systems modeled using a Taylor series and driven by phase-aligned or random phase multitone excitation. Nonlinear distortion figures-of-merit such as intermodulation ratio (IMR), adjacent channel power ratio, co-channel power ratio, and noise-to-power ratio, as well as the output power density can be straightforward computed using newly developed closed-form expressions. Simulation results of output power density obtained using the developed expressions for an -band commercial amplifier demonstrates the time efficiency and robustness of the proposed approach when compared to averaged data obtained using numerical simulators such as Agilent ADS. The comparison of the computed nonlinearity figures-of-merit with those previously published shows the importance of considering the fifth order when modeling nonlinear RF circuits/systems. The proposed analytical approach explicitly highlights the dependency of the normalized figures-of-merit relative to the standard two-tone IMR (IMR 2 ) to the input power and to the coefficients of the Taylor model contrary to third-order-based approaches.

No abstract

In this study, the authors propose a new numerically stable digital predistorter for the linearisation of RF Power amplifiers. The proposed predistorter is based on the parameterised Gegenbauer polynomials that can be optimised for maximum predistorter efficiency and stability under different input signal distributions. The robustness and the efficiency of the proposed predistorter are experimentally demonstrated and compared to the ones of previously published polynomial model‐based predistorters. The obtained results revealed exceptional numerical stability regardless of the input signal statistics, making the proposed predistorter suitable for the linearisation of multimode and broadband non‐linear wireless transmitters.

In this article, we propose a new set of basis functions based on Gegenbauer polynomials suitable for robust behavioral modeling of nonlinear dynamic systems. These polynomials can be optimized for maximum model identification stability under different input signal distributions. The efficiency and robustness of the proposed polynomial models are demonstrated and compared to the ones of previously published models. The obtained results revealed an exceptional numerical stability regardless of the input signal statistics, making the proposed new models suitable for multimode and broadband nonlinear wireless transmitters.