Regime Change: Bit-Depth versus Measurement-Rate in Compressive Sensing

TL;DR

This paper explores the tradeoff between measurement quantity and bit-depth in compressive sensing, revealing two regimes based on SNR and showing that low-bit measurements can be optimal in many practical scenarios.

Contribution

It introduces a theoretical framework distinguishing high and low SNR regimes in compressive sensing, highlighting the effectiveness of low-bit measurements in practical applications.

Findings

Two distinct regimes: measurement compression and quantization compression.

Low SNR favors more measurements with fewer bits per measurement.

Operating in the QC regime with 1-bit measurements can be optimal.

Abstract

The recently introduced compressive sensing (CS) framework enables digital signal acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed, the number of CS measurements required for stable reconstruction is closer to the order of the signal complexity than the Nyquist rate. To date, the CS theory has focused on real-valued measurements, but in practice, measurements are mapped to bits from a finite alphabet. Moreover, in many potential applications the total number of measurement bits is constrained, which suggests a tradeoff between the number of measurements and the number of bits per measurement. We study this situation in this paper and show that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement compression (MC) regime, a high SNR favors acquiring fewer measurements with more bits per measurement; in the quantization compression (QC) regime, a low SNR favors acquiring more measurements with fewer bits per measurement. A surprise from our analysis and experiments is that in many practical applications it is better to operate in the QC regime, even acquiring as few as 1 bit per measurement.