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One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin
One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin
Dissertations
Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …
Blind Separation For Intermittent Sources Via Sparse Dictionary Learning, Annan Dong
Blind Separation For Intermittent Sources Via Sparse Dictionary Learning, Annan Dong
Dissertations
Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on …
Blind Source Separation Using Dictionary Learning Over Time-Varying Channels, Anushreya Ghosh
Blind Source Separation Using Dictionary Learning Over Time-Varying Channels, Anushreya Ghosh
Theses
Distributed sensors observe radio frequency (RF) sources over flat-fading channels. The activity pattern is sparse and intermittent in the sense that while the number of latent sources may be larger than the number of sensors, only a few of them may be active at any particular time instant. It is further assumed that the source activity is modeled by a Hidden Markov Model. In previous work, the Blind Source Separation (BSS) problem solved for stationary channels using Dictionary Learning (DL). This thesis studies the effect of time-varying channels on the performance of DL algorithms. The performance metric is the probability …