@inproceedings{9b93c11ca69545468b9ed1c0561a395a,
title = "Blind Source Separation with L1 Regularized Sparse Autoencoder",
abstract = "Blind source separation of co-channel communication signals can be performed by structuring the problem with an over-complete dictionary of the channel and solving for the sparse coefficients, which represent the latent transmitted signals. L_{1} regularized least squares is a common approach to imposing sparsity on the latent signal representation while minimizing the reconstruction error. In this paper we propose an unsupervised learning approach for blind source separation using an L_{1} regularized sparse autoencoder with a softthreshold activation function at the hidden layer that is able to separate and fully recover multiple overlapping binary phase shift keying co-channel signals.",
keywords = "Autoencoder, blind source separation, co-channel separation, sparse coding, sparse recovery, sparse representation",
author = "Dabin, {Jason A.} and Haimovich, {Alexander M.} and Justin Mauger and Annan Dong",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th Wireless and Optical Communications Conference, WOCC 2020 ; Conference date: 01-05-2020 Through 02-05-2020",
year = "2020",
month = may,
doi = "10.1109/WOCC48579.2020.9114943",
language = "English (US)",
series = "2020 29th Wireless and Optical Communications Conference, WOCC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 29th Wireless and Optical Communications Conference, WOCC 2020",
address = "United States",
}