A Comprehensive Study of Adaptive LNA Nonlinearity Compensation Methods in Direct RF Sampling Receivers

Abstract

This paper studies the effects of nonlinear distortion of Low Noise Amplifier (LNA) for the multichannel direct-RF sampling receiver (DRF). The main focus of our work is to study and compare the effectiveness of the different adaptive compensation algorithms, including the inverse-based and subtract-based Least Mean Square (LMS) algorithm with a fixed and variable step size. The models for the compensation circuits have been analytically derived. As the major improvements, the effectiveness of the compensation circuits under the ADC quantization noise effect is evaluated. The bit-error-rates (BER) in dynamic signal-to-noise ratio (SNR) scenarios are calculated. We have proposed the use of variable step-size LMS (VLMS) to shorten the convergence time and to improve the compensation effect in general.

To evaluate and compare different compensation methods, a complex Matlab model of the Ultra high frequency (UHF) DRF with 4-QPSK channels was implemented. The simulation results show that all compensation methods significantly improve the receiver performance, with the convergence time of the VLMS algorithm does not exceed 5.10⁴ samples, the adjacent channel power ratios (ACPR) are reduced more than 30 dBc, and the BERs decrease by 2­–3 orders of magnitude, compared with the non-compensated results. The simulation results also indicate that the subtraction method in general has better performance than the inversion method.