Semi-Blind Multi-Modulus Algorithm for Joint CFO Estimation and Symbol Detection in MIMO-OFDM

Kabiru N. Aliyu*, Karim Abed-Meraim, Azzedine Zerguine, Abdulmajid Lawal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work introduces a new semi-blind multi-modulus approach for joint source separation and carrier frequency offset (CFO) estimation in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing communications. A hybrid semi-blind cost function based on the Least Squares (LS) and Multi-Modulus (MM) is designed for source recovery and CFO estimation. An alternating gradient descent optimization method is utilized to iteratively minimize the cost function along the search direction. Furthermore, an efficient initialization procedure is introduced to reduce the algorithm's complexity and ensure its convergence. The simulation results show that our method performs very well in terms of CFO estimation and data recovery.

Original languageEnglish
Title of host publication21st International Wireless Communications and Mobile Computing Conference, IWCMC 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages424-428
Number of pages5
ISBN (Electronic)9798331508876
DOIs
StatePublished - 2025
Event21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025 - Hybrid, Abu Dhabi, United Arab Emirates
Duration: 12 May 202416 May 2024

Publication series

Name21st International Wireless Communications and Mobile Computing Conference, IWCMC 2025

Conference

Conference21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Country/TerritoryUnited Arab Emirates
CityHybrid, Abu Dhabi
Period12/05/2416/05/24

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

  • Carrier Frequency Offset
  • MIMO-OFDM
  • Multi-Modulus
  • Semi-Blind Source Separation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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