RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing

TL;DR

This paper introduces RL-NCS, a reinforcement learning framework that adaptively allocates sensing resources in non-uniform compressed sensing for time-varying signals, improving reconstruction accuracy with fewer measurements.

Contribution

The paper presents a novel RL-based adaptive sensing scheme that predicts ROI coefficients using LSTM or previous data, optimizing measurement design for dynamic signals.

Findings

Significant performance improvement over traditional methods.

Effective for rapidly changing signals with fewer measurements.

Adaptive approach outperforms fixed sensing strategies.

Abstract

A reinforcement-learning-based non-uniform compressed sensing (NCS) framework for time-varying signals is introduced. The proposed scheme, referred to as RL-NCS, aims to boost the performance of signal recovery through an optimal and adaptive distribution of sensing energy among two groups of coefficients of the signal, referred to as the region of interest (ROI) coefficients and non-ROI coefficients. The coefficients in ROI usually have greater importance and need to be reconstructed with higher accuracy compared to non-ROI coefficients. In order to accomplish this task, the ROI is predicted at each time step using two specific approaches. One of these approaches incorporates a long short-term memory (LSTM) network for the prediction. The other approach employs the previous ROI information for predicting the next step ROI. Using the exploration-exploitation technique, a Q-network learns to choose the best approach for designing the measurement matrix. Furthermore, a joint loss function is introduced for the efficient training of the Q-network as well as the LSTM network. The result indicates a significant performance gain for our proposed method, even for rapidly varying signals and a reduced number of measurements.