A Novel Indirect Learning Digital Predistortion Architecture Only with In-Phase Component

This paper presents a novel indirect learning architecture (ILA) for digital predistortion (DPD), which uses only the I component of the output signal in the feedback path. This work saves half of the hardware cost of the feedback path and compensates for a portion of the distortion introduced by the I/Q imbalance. Experimental results show that the proposed method can achieve a similar linearization effect compared to the conventional ILA-DPD, in which both I and Q components of power amplifier (P A) output signal in the feedback path should be acquired to estimate the DPD coefficients. When there is I/Q imbalance, the linearization effect of the proposed method is better than the conventional ILA-DPD.