Comparison between Direct and Indirect Learnings for the Digital Pre-distortion of Concurrent Dual-band Power Amplifiers

Current radio-communication systems demand high linearity and high efficiency. The digital baseband pre-distorter (DPD) is a cost-effective solution to guarantee the required linearity without compromising the efficiency. In the design of a DPD for a single band power amplifier (PA), the position of the inverse system is exchanged during the identification procedure to avoid the necessity of a PA model within a cumbersome closed-loop process. However, in a practical environment where only an approximation to the inverse is achieved, the linearization capability is affected by shifting the post-inverse placed after the PA to a pre-inverse located before the PA. In DPD intended for concurrent dual-band PAs, an additional advantage of such approach is that the post-inverse identifications for each band are completely independent of each other. This work performs a comparative analysis between two learning architectures applied to the linearization of two concurrent dual-band PAs stimulated by 2.4 GHz Wi-Fi and 3.5 GHz LTE signals. For the first PA, an exact PA model is known and the replacement of a post-inverse to a pre-inverse produces only negligible degradation in linearity. For the second PA, only an approximate PA model is available and the accuracy of such PA model produces a major impact on the linearization capability than the shifting of the inverse.