4 accepted papers
In this paper, we address the problem of one-bit compressed sensing using the data-driven deep learning approach. Our approach uses an untrained neural network to reconstruct sparse vectors from their one-bit measurements. We define a new cost function using the untrained network, which maximizes th…
In this paper, we address the classical problem of one-bit compressed sensing. We present a deep learning based reconstruction algorithm that relies on a generative model. The generator which is a neural network, learns a mapping from a low dimensional space to a higher dimensional set comprising of…
We consider the problem of sparse signal reconstruction from noisy 1-bit compressed measurements using a statistically dependent signal, as an aid. We assume that this signal does not share joint sparse representation with the sparse signal and call it a heterogeneous side-information. We assume tha…
In this paper, we consider the problem of sparse signal detection with compressed measurements in a Bayesian framework. Multiple nodes in the network are assumed to observe sparse signals. Observations at each node are compressed via random projections and sent to a centralized fusion center. Motiva…