Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling

TL;DR

This paper introduces learning-based adaptive sampling algorithms that significantly reduce the number of samples needed for accurate signal reconstruction, outperforming traditional methods in efficiency and accuracy.

Contribution

It proposes novel adaptive sampling algorithms, Individual Chasing and Centroid Chasing, that reduce sampling requirements beyond conventional compressive sensing techniques.

Findings

46% fewer samples needed for accurate reconstruction

Up to 57% reduction in reconstruction error

Algorithms are independent of specific reconstruction methods

Abstract

Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow $46\%$ less number of samples for accurate signal reconstruction and achieve up to $57\%$ smaller signal reconstruction error under the same noise condition.