On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers

In this article, we present a dynamic model sizing approach for digital predistortion (DPD) of broadband radio-frequency power amplifiers. By employing a novel model structure adaptation algorithm, the DPD model structure can be adaptively adjusted during its real-time deployment to keep the optimum size and complexity under different operation conditions. Power consumption of DPD can be reduced by on-demand automatic model structure adaptation instead of reusing the same model structure for all power levels and band allocations. To realize dynamic model sizing, the adaptation algorithm explores new potential terms based on prior knowledge of the model structure and prunes the DPD model with a stepwise backward regression method. Experimental results show that the algorithm can quickly find the optimum model structure when the operation condition changes. During the adaptation, it can also maintain robust linearization performance with a relatively low computational complexity and thus demonstrates itself as a suitable solution to the linearization of future broadband wireless systems.