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

Ubaid M. Al-Saggaf, Muhammad Moinuddin*, Syed Saad Azhar Ali, Syed Sajjad Hussain Rizvi, Muhammad Faisal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationWearable and Neuronic Antennas for Medical and Wireless Applications
Publisherwiley
Pages1-9
Number of pages9
ISBN (Electronic)9781119792581
ISBN (Print)9781119791805
DOIs
StatePublished - 1 Jan 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Scrivener Publishing LLC.

Keywords

  • 5G
  • FBMC
  • MMSE
  • Machine learning
  • Multicarrier communications
  • Multiuser communications
  • OFDM
  • equalization

ASJC Scopus subject areas

  • General Engineering

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