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This paper proposes a methodology to ensure linear amplification of a load modulated balanced amplifier (LMBA) while keeping the power efficiency as high as possible over a frequency band ranging from 1.8 to 2.4 GHz and where the transmitted signals can present different bandwidth configurations. The proposed reconfigurable linearization methodology consists of, in a first step, tuning some free-parameters (with dependence on the signal bandwidth and frequency of operation) of the load modulated balanced amplifier (LMBA) to trade-off linearity and power efficiency. In a second step, two multi-purpose adaptive digital predistortion (DPD) linearizers are considered, properly combined with crest factor reduction (CFR) techniques, to meet the required linearity specifications. Either a DPD based on artificial neural networks or a DPD based on polynomials can be selected taking into account the compromise between computational complexity and linearization performance. Experimental results will validate the proposed methodology to guarantee the linearity levels (ACPR<-45 dBc and EVM<1%) with high power efficiency in an LMBA under dynamic transmission, where both the signal bandwidth (from 20 MHz and up to 200 MHz instantaneous bandwidth) and frequency of operation (in the range of 1.8 to 2.4 GHz) change.Index Terms-artificial neural network (ANN), digital predistortion (DPD), load-modulated balanced amplifiers (LMBA), power efficiency.

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