In this paper, a comprehensive system behavioral model of frequency-interleaved analog-to-digital converters (FI-ADCs) is presented. The model addresses mismatch errors due to imperfect channel separation, incomplete anti-aliasing, in-phase/quadrature branch imbalances, and jitter-induced distortions. Using this model, an integrated analysis of combined channel mismatch effects is provided. Given error parameters or their distributions, closed-form expressions of the expected output signal-to-noise ratio and image rejection ratio are formulated for FI-ADCs with an arbitrary number of channels. The inherent circuit behaviors, sampling clock jitters, and local oscillator phase noise effects are also discussed. Based on the analysis and modeling, a discrete-time equivalent model for FI-ADC systems is derived. Therefrom, an integrated channel mismatch compensation scheme is proposed that contrasts existing signal recovery approaches which calibrate specific types of errors separately. The performance of this novel method is compared with conventional solutions using extensive simulations. Very favorable numerical results are observed.

A multichannel bandwidth‐interleaved (BI) framework with digital back‐end correction of mismatch errors is explored to simultaneously increase the sample‐rate and bandwidth of the ADCs. In addition to the consideration of channel frequency‐response mismatch and aliasing error associated with sampling, the autocorrelation matrix of magnitude response is introduced to evaluate the performance of overlapping cancellation between the nearby subbands. Simulation results and analytical calculations are provided to verify the efficacy of the proposed correction approach.

This paper connects the linear steady-state systematic error models of the time- and frequency-interleaved analog-to-digital converters (ADCs). Exposing their relations is of importance because estimation and compensation methods developed for one architecture may therefore apply to the other. Most critical impairments in both ADC structures include static mismatches and random jitter. The former has been well studied and can be generalized to the model connection, whereas not much is known regarding the latter. To support designers becoming more capable of making optimal design and architectural decisions on parallel ADCs, comprehensive phase noise analysis and comparison are carried out to reveal the distinctions between these two sampling architectures. Design examples with considerations are also provided for demonstration purposes.

The non-ideal circuit implementations cause a significant degradation in performance of TIADC (time-interleaved analog-to-digital converter) system. In this paper, a behavioral model for TIADC based on Volterra series is proposed to model the dynamic nonlinearities in TIADC. The time-domain and frequency-domain expressions of the behavioral model based on hybrid Volterra series are derived firstly. Then the discrete-time equivalent model is proposed by transforming hybrid TIADC system to a discrete time one based on discrete-time Volterra series only. The derivations give a theoretical foundation to use discrete-time Volterra series to model mixed-domain TIADC system, which makes it possible to make full use of the related existing derivations, conclusions and methodologies on discrete-time Volterra series. We also summarize some common special cases of Volterra series to provide a practical guideline for ADC and TIADC practitioners. We introduce the main features of these models and their relationship with Volterra series. The simulation and experimental results show the effectiveness of the proposed model.

A novel digital adaptive blind calibration technique for frequency-interleaved analog-to-digital converters (FI-ADCs) with arbitrary numbers of channels is proposed. It performs the estimation and correction of multiple inherent circuit deficiencies alternately and recursively in the background, including spectral leakage, harmonic folding, jitter, I/Q mirror and aliasing images, and suffices to deliver projected calibration capabilities with de-liberately chosen initial guesses. We are of the opinion that this is the first study to address the joint channel identification and error compensation problem encountered in the FI-ADC design. For the efficient implementation, an analytical time-interleaving-like equivalent model is formulated such that the number of unknown mismatch parameters is significantly reduced, and signals devoted to cyclic estimation are maximally decimated. This derived model not only propounds a better perspective for comprehension and interpretation of the FI system mechanism but also enables su-fficient scalability and flexibility of the developed calibration fra-mework. Extensive simulation results further show that this novel architecture achieves a good balance between the computational complexity and convergence, and provides an exceptional and relatively flat effective number of bit (ENOB) performance over a wide bandwidth.

Given tremendously growing security and privacy concerns, there is a substantial surge in demand for covert communication techniques. Traditional methods, constrained by static configurations and an inability to leverage historical data, suffer from inefficiencies in resource utilization, vulnerability to evolving threats, and limited adaptability to interference. This article introduces a Generative Artificial Intelligence (GAI)-driven framework to overcome such limitations, integrating advanced models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Generative Diffusion Models (GDMs) into covert communication systems. By employing unsupervised learning, the framework dynamically optimizes transmission parameters—including power allocation, modulation schemes, and frequency hopping—in real time, enabling adaptive responses to channel conditions and historical patterns. Through this optimization, the proposed approach enhances security via unpredictable transmission patterns, reduces bit error rates with optimized modulation and coding schemes, and strengthens resistance to interference and interception through noise-like signal generation. In addition to these advancements, the paper identifies key challenges and further outlines future directions. This work underscores GAI’s transformative potential in balancing concealment, efficiency, and adaptability for next-generation covert communication systems.