Physical Reservoir Signal Acquisition for Sub-Nyquist Waveform Reconstruction

2026-07-15

AI Summary

This work addresses the high cost and power consumption of conventional Nyquist-rate sampling for wideband signals, which demands high-speed analog-to-digital converters (ADCs). To overcome this limitation, the authors propose a novel paradigm termed Reservoir Signal Acquisition (RSA), which, for the first time, leverages a physical reservoir not as a computational unit but as a dynamic measurement device. By exploiting the rich intrinsic dynamics of the reservoir, unknown broadband signals are mapped into diverse low-rate measurements. This approach inherently integrates compressive sensing capabilities, enabling accurate reconstruction of sparse signals even when the number of measurement channels is far below the sub-Nyquist rate. Using a silicon photonic reservoir circuit, data-driven calibration, and a model-free reconstruction algorithm, the authors experimentally demonstrate successful recovery of a 12.5 GHz radio-frequency signal using only low-speed ADCs—surpassing the single-channel Nyquist limit by a factor of four—and thereby validate RSA’s effectiveness and innovation in sub-Nyquist wideband signal acquisition.

Abstract

Physical reservoir computing has traditionally exploited the dynamics of physical systems for computation, enabling tasks such as inference, classification, and prediction. Here, we introduce a fundamentally different paradigm for exploiting physical reservoirs, termed "reservoir signal acquisition" (RSA), in which a physical reservoir serves as a dynamical measurement device rather than a computational engine. In RSA, the reservoir transforms an unknown broadband waveform into a diverse set of measurements, enabling waveform reconstruction from low-rate samples beyond the Nyquist limit of any individual acquisition channel. We show that exact reconstruction of arbitrary broadband signals is achieved when the number of measurement channels satisfies $M \geq N_R$, where $N_R$ is the undersampling ratio. Moreover, spectrally or temporally sparse signals can be recovered even when $M \ll N_R$, demonstrating a compressed-sensing capability that naturally emerges from the diversity of reservoir dynamics. We experimentally validate RSA using a silicon photonic reservoir circuit. With a data-driven calibration requiring no physical model of the device, we reconstruct radio-frequency signals up to 12.5 GHz using only low-rate analog-to-digital converters (ADCs), corresponding to four times the Nyquist frequency of each ADC. These results establish RSA as a new signal acquisition paradigm based on physical reservoirs, extending their role from computation to sub-Nyquist acquisition of broadband waveforms.

Problem

• sub-Nyquist sampling
• waveform reconstruction
• physical reservoir computing
• compressed sensing
• broadband signals

Innovation

• reservoir signal acquisition
• sub-Nyquist sampling
• physical reservoir computing
• compressed sensing
• silicon photonics