IQ Mismatch Compensation Using Iterative IRR Optimization

The patent transforms the complex multi-parameter IQ mismatch compensation problem into a simplified one-dimensional optimization by changing parameters from complex I/Q path parameters to real-valued pre-compensator parameters (α, β, γ, δ) that can be optimized using gradient ascent on a single performance metric (IRR), reducing computational complexity while maintaining optimization effectiveness

The patent implements feedback by using the calculated IRR value as a performance metric that guides the gradient ascent optimization process, where the IRR calculation feedback from each parameter set informs the next parameter adjustment, creating a closed-loop optimization system that converges to optimal values

The patent extracts and prioritizes the most critical performance metric (IRR) from multiple possible metrics (IRR, SINR, SImR), focusing optimization efforts on maximizing IRR as the primary objective function. This extraction approach simplifies the optimization problem by dealing with one dominant metric rather than simultaneously optimizing multiple competing metrics

The patent segments the optimization process into distinct phases: first optimizing pre-compensator parameters to maximize IRR, then using those optimized values as fixed parameters for subsequent signal processing. This segmentation allows each phase to be optimized independently, reducing overall optimization complexity