Data-Driven Digital Adaptive Timing Mismatch Calibration for Sub-Nyquist Wideband Sampling

The employment of sub-Nyquist sampling enables the adaptation to the ever-increasing bandwidth demands of wireless communication systems. Multicoset sampling is a nonuniform sub-Nyquist sampling technique that can be realized by using a multi-channel architecture. Nevertheless, the performance of multicoset sampling is significantly affected by timing mismatches among sub-ADCs. Traditional analog calibration scheme mitigates timing mismatch errors by adjusting the sampling clock in the front-end, but its performance is suboptimal due to the difficulty in selecting an appropriate initial step size and the sequential calibration process. In this paper, a data-driven digital adaptive timing mismatch calibration approach is proposed that uses real-time measurement data only to calculate calibration parameters without changing hardware circuits, making it effective for tracking variations in mismatches among multiple channels. Based on the nonlinear relationship between the timing mismatches and the normalized mean squared error of the output spectrum, the proposed data-driven calibration scheme jointly computes calibration parameters among multiple channels in parallel through a series of dynamic linearization data models with a time-varying pseudo-partial derivative parameter estimation procedure, and then adaptively adjusts the sampling matrix in the reconstruction to correct unwanted distortions in the output spectrum. To further improve the calibration performance, particle swarm optimization algorithm is exploited to generate parameter vectors used in the proposed data-driven calibration scheme. Numerical analyses indicate that the proposed data-driven digital calibration scheme can eliminate timing mismatches with fewer calibration iterations and enhanced accuracy.