In this paper, a behavioral model with multi-status is represented to describe electrothermal memory of power amplifiers. In the model, a multi-point linearity approximation method is introduced to estimated the model coefficients at random status. The parameters of the new model can be identified by traditional identification algorithms such as recursive least square (RLS) with the input and feedback data at predetermined status points captured by the digital predistortion (DPD) system. Compared with traditional models, the coefficients estimation speed for the new model at new status is very fast. The final experiment result proves that the multi-status model can efficiently ameliorate the performance deterioration caused by electrothermal memory in the the DPD system, and at the same time it still keeps very high DPD performance.
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