Online censoring for real-time digital predistortion linearization of power amplifiers

Digital predistortion (DPD) is an effective power amplifier (PA) linearization technique improving the system energy efficiency. At this point, real-time DPD adaptation is still an open issue due to the high computational complexity during the coefficients estimation procedure. Online censoring approach, which is effective in reducing the redundant data samples, can be applied in the DPD coefficients estimation. In this paper, we propose online data-adaptive, least mean-square (LMS)-type algorithms to reduce the overall computational complexity. The proposed algorithms entail simple, closed-form expression and simulation results validate the effectiveness of proposed algorithm.

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