Orthogonal Scalar Feedback Digital Pre-Distortion Linearization

Abstract

Digital pre-distortion (DPD) systems are employed to reduce intermodulation at the output of intrinsic non-linear RF power amplifiers. Typical DPD algorithms use time-domain linear regression to calculate the model coefficients used to compensate the amplifier’s distortion. This solution has high complexity and limited performance. This paper presents an innovative DPD system where the feedback information is a scalar measurement taken from the feedback signal. Different feedback information can be obtained from the distorted signal, e.g., the adjacent channel leakage rejection or the spectral mask margin. The DPD model coefficients estimation becomes a generic numerical optimization problem and is conducted iteratively in a coefficient-by-coefficient basis. A new coefficient orthogonalization algorithm removes the interaction between orders, resulting in a faster convergence time and lower output power variation across the iterations. The numerical optimization problem is simplified, since the nonlinear model becomes orthogonal in the intermodulation domain, i.e., the adjustment of given coefficient does not affect the others. Simulations and experimental results show that the proposed algorithm can effectively and efficiently compensate the nonlinear distortion and reduce the spectral regrowth.