Adaptive Non-Uniform Sampling of Bandlimited Signals via Algorithm-Encoder Co-Design

TL;DR

This paper introduces an adaptive non-uniform sampling method for bandlimited signals using an algorithm-encoder co-design approach, enabling efficient sampling with fewer measurements while ensuring accurate reconstruction through local convergence conditions.

Contribution

It presents a novel local energy-based sampling condition and a variable-bias integrate-and-fire encoder that adaptively samples signals based on their local variation, improving efficiency over traditional methods.

Findings

Significant reduction in sampling density compared to uniform sampling.

Accurate reconstruction achieved even below Nyquist rate.

Demonstrated effectiveness on synthetic and real-world signals.

Abstract

We propose an adaptive non-uniform sampling framework for bandlimited signals based on an algorithm-encoder co-design perspective. By revisiting the convergence analysis of iterative reconstruction algorithms for non-uniform measurements, we derive a local, energy-based sufficient condition that governs reconstruction behavior as a function of the signal and derivative energies within each sampling interval. Unlike classical approaches that impose a global Nyquist-type bound on the inter-sample spacing, the proposed condition permits large gaps in slowly varying regions while enforcing denser sampling only where the signal exhibits rapid temporal variation. Building on this theoretical insight, we design a variable-bias, variable-threshold integrate-and-fire time encoding machine (VBT-IF-TEM) whose firing mechanism is explicitly shaped to enforce the derived local convergence condition. To ensure robustness, a shifted-signal formulation is introduced to suppress excessive firing in regions where the magnitude of the signal amplitude is close to zero or the local signal energy approaches zero. Using the proposed encoder, an analog signal is discretely represented by time encodings and signal averages, enabling perfect reconstruction via a standard iterative algorithm even when the local sampling rate falls below the Nyquist rate. Simulation results on synthetic signals and experiments on ultrasonic guided-wave and ECG signals demonstrate that the proposed framework achieves substantial reductions in sampling density compared to uniform sampling and conventional IF-TEMs, while maintaining accurate reconstruction. The results further highlight a controllable tradeoff between sampling density, reconstruction accuracy, and convergence behavior, which can be navigated through adaptive parameter selection.