Concurrent dual-band digital predistortion for power amplifier based on orthogonal polynomials

Single-Model Single-Feedback Digital Predistortion for Concurrent Multi-Band Wireless Transmitters

“…'() X is the input matrix generated from the input , containing all linear and nonlinear model terms processed by the band-limiting functions , which is shown in (22) in the bottom of this page. Because the model is shared among all the bands, the matrix assembled at each band are combined together, that is,…”

“…L. Ding et al [21] simplified the 2D-DPD model by proposing two efficient implementation schemes based on look-up tables. C. Quindroit et al [22], [23] and Yang et al [24] proposed an orthogonal 2D-DPD to improve the stability of the conventional model. M. Younes et al [25] and M. Rawat et al [26] employed the 2D-DPD model to compensate for hardware impairments.…”

FPGA-based test bed for measurement of AM/AM and AM/PM distortion and modeling memory effects in RF PAs

“…However, this requires knowledge of the PA behavior a priori, and the PA's distortion is usually characterized in terms of AM/AM and AM/PM. Thus, the measurement of the distortion curves is crucial in the application of PAs in telecommunications or digital predistortion (DPD) [13][14][15]. In this regard, the AM/AM and AM/PM distortion curves are the preliminary figure of merit on complex signals by using DPD techniques [16].…”

Modeling memory effects in RF power amplifiers applied to a digital pre-distortion algorithm and emulated on a DSP-FPGA board

“…Unfortunately, its inherent nonlinearity causes inband distortion and intermodulation products that spreads out over neighbor channels. That way, to mitigate undesired intermodulation and distortion effects, recent advances related to linearization techniques have been developed, mainly to compensate memory effects induced by real PAs [2,3]. These issues have been partially solved introducing modeling techniques for PAs, such as: memory or memoryless models [4,5], polynomial models [6], neural networks [7,8], others techniques taking into account memory effects based on Volterra series [9][10][11][12][13], etc., all of them providing a proper way to capture the nonlinearity order and memory depth.…”