Adaptive Digital Signal Predistortion for Nonlinear Communication Systems Using Successive Methods

Abstract

Highly-efficient operation of communication systems requires effective compensation of nonlinear distortion with memory. The main contributor to nonlinearity is the high-power amplifier (HPA) when operated close to saturation. This results in two major detrimental effects: spectral regrowth causing interference in adjacent frequency bands and in-band distortion in the form of constellation warping and clustering. This paper introduces a novel family of adaptive digital signal predistortion schemes that successively modifies the HPA input to drive nonlinear distortion with memory toward zero. This family of schemes is capable of suppressing the spectral regrowth and in-band distortion simultaneously, while keeping the HPA operating efficiently close to saturation. In addition, the proposed solution offers the system designer a beneficial tunability feature to select the levels of suppression. Reduced-complexity Volterra model is adopted and is implemented on-the-fly to cope with systems with high degree of nonlinearity and large memory span. Furthermore, the proposed schemes are made adaptive by applying stochastic gradient method offline during training phase to effectively deal with nonlinear systems whose characteristics are unknown a priori. Extensive computer simulations demonstrate that the proposed adaptive family of predistortion schemes approaches the performance of the perfectly predistorted solution, and can considerably outperform techniques based on adaptive inverse, commonly adopted in the literature.