@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",
}