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Flower pollination algorithm-based I/Q phase imbalance compensation strategy.

Jie MengHoujun WangPeng YeYu ZhaoHao ZengLianping Guo
Published in: The Review of scientific instruments (2021)
For wideband receiver systems, it is challenging to compensate the in-phase/quadrature (I/Q) phase mismatch by traditional methods, especially with a time delay deviation (TDD) between the I/Q channels. Considering the above situation, this paper proposes a full-scale I/Q phase imbalance model concerning TDD. The model divides phase mismatch into two parts, i.e., the linear phase (LP) part and the nonlinear phase part, and compensates each part with the corresponding compensation module separately. The design strategy of the compensation module is innovatively transformed into a constrained nonlinear optimization problem, and a metaheuristic algorithm, the flower pollination algorithm (FPA), is utilized to be the optimizer. The results of the contrast simulation with the LP elimination method show the efficiency of the proposed method. In addition, the superiority of the FPA-based structure is verified by comparing with other metaheuristic algorithms, the artificial bee colony technique, the bat algorithm, and the differential evolution algorithm, in terms of the compensation accuracy, algorithm stability, runtime consumption, and convergence performance. Ultimately, the image rejection ratio improvement on the actual platform after compensation is measured, which validates the proposed compensation structure and the corresponding optimization method practically, and the FPA is still the best choice among the competent optimizers.
Keyphrases
  • machine learning
  • deep learning
  • magnetic resonance imaging
  • high throughput