@inproceedings{6699f2fbd15c469e9a2b6dde110783f6,
title = "LMS for truncate CSI feedback in massive MIMO",
abstract = "In this work, a new methodology for estimation of channel state information (CSI) is presented dependent upon the idea of Least Mean Square (LMS). The strategy is formed to massive MIMO frameworks through feedback communications. The technique is intended to reduce the amount of feedback communication information contains in the CSI data block from the mobile client to the base station. The data amount of encoder at the mobile client side is reduced utilizing the discrete cosine transform (DCT) on the CSI matrix. The retrieved CSI data block toward the base station side employments an IDCT (Inverse DCT) with recreate those CSI matrix. Our simulations indicate better results considering normalized mean-square-error (NMSE) as performance measurement against existing methodologies.",
keywords = "Channel state information (csi), Compressed sensing (cs), Least mean square (lms), Massive mimo, Reconstruction",
author = "Ali Almohammedi and Mohamed Deriche",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019 ; Conference date: 22-08-2019 Through 23-08-2019",
year = "2019",
month = aug,
doi = "10.1109/iCCECE46942.2019.8941771",
language = "English",
series = "Proceedings - 2019 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "241--246",
editor = "Miraz, \{Mahdi H.\} and Excell, \{Peter S.\} and Andrew Ware and Safeeullah Soomro and Maaruf Ali",
booktitle = "Proceedings - 2019 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2019",
address = "United States",
}