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The linearization of radio frequency power amplifiers (PAs) for wireless communication systems can be performed by a digital baseband predistorter (DPD). The DPD design includes the parameter identification of a post-inverse or a pre-inverse model. Such process depends on measurements of discrete-time complex-valued envelopes. With the adoption of larger envelope bandwidths and the PA operation at stronger nonlinear regimes, the requirements on the sampling frequency are very stringent. To relax the specifications on the analog-to-digital and digital-to-analog converters, a band-limited memory polynomial can be employed. To reduce the amount of calculations, polynomials can be replaced by linearly interpolated look-up tables (LUTs). Traditionally, a non optimal two step procedure is performed to identify the values to be stored in the LUTs. This work contribution is to propose an optimal identification technique that computes directly the values to be stored in the LUTs. The reported case study uses an LTE OFDMA envelope and Matlab simulation results show that the modeling accuracy can be significantly improved by the adoption of the proposed technique instead of the traditional one, quantified by reductions in normalized mean square error and adjacent channel power ratio of up to 10.8 dB and 12.4 dB, respectively.

Optimal Parameter Identification for Look-up Table based Band-limited Memory Polynomial Model using Direct and Indirect Learnings