Machine Learning Aided Channel Equalization in Filter Bank Multi-Carrier Communications for 5G

• Main Authors: Al-Saggaf, U.M., Moinuddin, M., Azhar Ali, S.S., Hussain Rizvi, S.S., Faisal, M.
• Format: Book
• Published: wiley 2022
• Online Access: http://scholars.utp.edu.my/id/eprint/37628/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151613961&doi=10.1002%2f9781119792581.ch1&partnerID=40&md5=0d56c52c5b2c75192fdd57a4dbc295f4
• Institution: Universiti Teknologi Petronas

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.