@inproceedings{1c3f4f9e42f64d07b0ac11be90a778c7,
title = "Compressed sensing for wideband HF channel estimation",
abstract = "Compressive sensing theory is suitable for sparse channel estimation, since the acquired measurement can be reduced in comparison with linear estimation methods. In this paper, we analyze the wideband HF channel estimation. Experimental results demonstrate that this channel is sparse in the delay spread domain. Moreover, the use of sparse recovery algorithms achieves better results in terms of Mean-Square Deviation than the Least Square algorithm.",
keywords = "compressive sensing, sparse channel estimation, wideband HF channel",
author = "Marques, \{E. C.\} and N. MacIel and Naviner, \{L. A.B.\} and Hao Cai and Jun Yang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th International Conference on Frontiers of Signal Processing, ICFSP 2018 ; Conference date: 24-09-2018 Through 27-09-2018",
year = "2018",
month = nov,
day = "28",
doi = "10.1109/ICFSP.2018.8552050",
language = "English",
series = "2018 4th International Conference on Frontiers of Signal Processing, ICFSP 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2018 4th International Conference on Frontiers of Signal Processing, ICFSP 2018",
}