relation: https://khub.utp.edu.my/scholars/17199/
title: Machine Learning Aided Channel Equalization in Filter Bank Multi-Carrier Communications for 5G
creator: Al-Saggaf, U.M.
creator: Moinuddin, M.
creator: Azhar Ali, S.S.
creator: Hussain Rizvi, S.S.
creator: Faisal, M.
description: Multi-carrier communications (MC) have gained a lot of interest as they have shown better spectral efficiency and provide flexible operation. Thus, the MC are strong candidates for the fifth generation of mobile communications. The Cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) is the most famous technique in the MC as it is easy to implement. However, the OFDM has poor spectral efficiency due to limited filtering options available. Thus, to enhance spectral efficiency, an alternative to OFDM called Filter bank multicarrier (FBMC) communication was introduced, which has more freedom of filtering options. On the other hand, the FBMC preserves only real orthogonality for the waveforms, resulting in imaginary interference. Hence, the equalization in FBMC has to deal with this additional interference which becomes challenging in multiuser communication. In this chapter, the aim is to deal with this challenge. © 2022 Scrivener Publishing LLC.
publisher: wiley
date: 2022
type: Book
type: PeerReviewed
identifier:   Al-Saggaf, U.M. and Moinuddin, M. and Azhar Ali, S.S. and Hussain Rizvi, S.S. and Faisal, M.  (2022) Machine Learning Aided Channel Equalization in Filter Bank Multi-Carrier Communications for 5G.     wiley, pp. 1-9.  ISBN 9781119792581; 9781119791805     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151613961&doi=10.1002%2f9781119792581.ch1&partnerID=40&md5=0d56c52c5b2c75192fdd57a4dbc295f4
relation: 10.1002/9781119792581.ch1
identifier: 10.1002/9781119792581.ch1