Blind Source Separation with L1 Regularized Sparse Autoencoder
Document Type
Conference Proceeding
Publication Date
5-1-2020
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.
Identifier
85091926963 (Scopus)
ISBN
[9781728161242]
Publication Title
2020 29th Wireless and Optical Communications Conference Wocc 2020
External Full Text Location
https://doi.org/10.1109/WOCC48579.2020.9114943
Recommended Citation
Dabin, Jason A.; Haimovich, Alexander M.; Mauger, Justin; and Dong, Annan, "Blind Source Separation with L1 Regularized Sparse Autoencoder" (2020). Faculty Publications. 5310.
https://digitalcommons.njit.edu/fac_pubs/5310
