Statistical Linearization of Phased Arrays Using Power Adaptive Power Amplifier Model

Abstract

Phased arrays used in millimeter-wave systems challenge the concept of power amplifier (PA) linearization by digital predistortion (DPD). This is due to the shared digital path and inaccuracies in analog beamforming and other component variations. However, the group behavior of multiple parallel nonlinear branches can be expected to be more predictable due to averaging effect compared to a single branch behavior. In this paper, we use a power adaptive nonlinear model to mimic the average behavior of a single PA and utilize the probability distribution of the input power of each individual PA to approximate the expected nonlinear behavior of the array over-the-air. The approximated array response is used for the DPD training. The simulation results indicate that the proposed approach provides good linearization performance for large arrays that have varying amplitude and phase weights.

Statistical Digital Predistortion of 5G Millimeter-Wave RF Beamforming Transmitter Under Random Amplitude Variations

“…8. For average array response estimation, we assume that the number of equally spaced intervals of the histogram is equal to the number of array elements, i.e., N V = N A [27]. The mean power adaptive model is considered as one virtual PA which is excited by an input signal with different power levels.…”

“…When the steering angle is random, also the variations in the DPD coefficients become random [26]. Using this assumption, an SISO-based statistical DPD (SDPD) approach was presented in [27] that can tolerate the changes in the array nonlinear response due to the rapid changes in beamsteering directions with no need for DPD coefficients readaptation. The SDPD approach uses the PA input power variations resulting from beamforming variations, to approximate the average array response for DPD model training.…”

“…The used approach provides decent linearization performance to the spatially distributed user directions through only one-time trained DPD coefficients. However, the reported analysis in [27] was based on the assumption that all PAs of the array exhibit similar nonlinear characteristics, and the analysis was limited to simulations only. In this article, we extend the concept of [27] to cover also the variations in individual PA models and provide the theoretical basis for array nonlinear behavior dependency on beamforming variations.…”

“…However, the reported analysis in [27] was based on the assumption that all PAs of the array exhibit similar nonlinear characteristics, and the analysis was limited to simulations only. In this article, we extend the concept of [27] to cover also the variations in individual PA models and provide the theoretical basis for array nonlinear behavior dependency on beamforming variations. The overall concept is demonstrated on an mmWave hardware platform with extensive set of OTA measurements using 5GNR waveform.…”

Can PA Governance Improve PA Outcomes: A Review of the Debates

“…Power (an important issue in PA governance) is central to the differences in the various types of governance, and a larger extent, instrumental to the success or failure of associated PA objectives. In a PA, power determines who (citizens, media, public sector, civil societies, and the government) controls the largest terrain in power distribution (Graham et al, 2003;Khan et al, 2019). It is now a common practice for power to be shared between government and non-government actors because governance is all-inclusive (concerned with how other actors including civil society organization, have inputs into decision making on matters of public concern and how different scales of government interact) (Graham et al, 2003;Kothari, 2006).…”