PROJECT TITLE :
A General Digital Predistortion Architecture Using Constrained Feedback Bandwidth for Wideband Power Amplifiers
Digital predistortion (DPD) is one of the foremost effective techniques to mitigate the distortions caused by power amplifier (PA) nonlinearity and memory effects. As the input signal bandwidth will increase, the desired bandwidth on the DPD feedback channel becomes even larger, i.e., normally five times the signal bandwidth. But, the DPD feedback bandwidth is often restricted by the nonideal electronic components, e.g., the anti-aliasing filter and associated circuits, which therefore introduce bandwidth mismatch between the PA model basis functions and the feedback signal, and thus degrade the linearization performances of the DPD. This paper presents a general DPD design for wideband PA systems with constrained feedback bandwidth. By using linear operations to cancel the bandwidth mismatch between the proposed model and therefore the PA feedback signal, the total-band PA model parameters can be estimated with bandwidth-restricted observations. This estimated PA model is subsequently used with the PA input signal to extract the DPD function by applying the direct learning algorithms. The proposed DPD design reduces the feedback bandwidth to but two times that of the input signal, whereas it maintains its linearization performance, as in the complete-band case. Experiments are performed on the twenty- and a hundred-MHz long-term evolution advanced signals to demonstrate the effectiveness of the proposed PA behavior modeling and DPD linearization performances with restricted feedback bandwidth.
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