A Novel Iterative Method for I/Q Imbalance Estimation and Calibration in Direct-Conversion Transmitters

Direct-conversion transmitters (DCTs) have been widely employed in modern communication systems due to their advantages of low cost and high spectral efficiency. However, the inherent problem of in-phase/quadrature (I/Q) imbalance in this architecture significantly limits overall system performance. This paper addresses the I/Q imbalance problem in DCTs by proposing an efficient estimation and calibration method based on an improved swarm intelligence optimization algorithm, which is implemented in software. The algorithm dynamically optimizes the iterative process of pre-distortion coefficients, thereby improving the efficiency of imbalance parameter estimation. To further mitigate the limitations of conventional particle swarm optimization (PSO) algorithms in terms of convergence speed and accuracy, we design a nonlinear exponential parameter update strategy with adaptive focusing characteristics. This strategy adaptively adjusts learning factors during iteration by dynamically evaluating changes in the image rejection ratio (IRR), effectively balancing local exploitation and global exploration capabilities to enhance convergence efficiency. Experimental results demonstrate that the proposed method achieves efficient and precise I/Q imbalance calibration for transmitters, with the IRR improved to over 65 dB for single-tone signals and over 53 dB for broadband signals.