RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing
This work addresses improved signal recovery in compressed sensing for applications like medical imaging or communications, but it is incremental as it builds on existing RL and LSTM methods.
The paper tackled the problem of non-uniform compressed sensing for time-varying signals by introducing RL-NCS, a reinforcement learning framework that optimally distributes sensing energy between region-of-interest and non-region-of-interest coefficients, resulting in significant performance gains even with reduced measurements and rapidly varying signals.
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.