Machine learning classification of hermite gaussian beams for 5G and beyond free-space optical backhaul links

A. Chehri, A. Ahmed, M.Z. Shakir

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Free space optical (FSO) communication offers an excellent opportunity to develop energy-efficient, secure, and ultrafast data links for 5G and beyond applications, including heterogeneous networks with massive connectivity and wireless backhauls for cellular systems. However, the effect of an optical beam's pointing inaccuracy combined with the impact of climate factors must be considered in the FSO communication system. In this paper, we first evaluate the performance reliability and availability of NRZ-based mode division multiplexing (MDM)-FSO backhaul. In particular, a single wavelength laser is used to transmit four different optical beams, using four different wavelengths. It also explores and classifies four beams used for capacity enhancement in mode division multiplexed MDM-FSO backhaul links. Several Machine Learning (ML) models are used to classify the four optical modes. Results indicate successful transmission of 80 Gbps. Furthermore, the primary findings indicate that the ML model exhibits an impressive accuracy rate of approximately 97% in classifying four distinct beams.
Original languageEnglish
Title of host publicationIEEE Conference on Global Communications (GLOBECOM)
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)9798350310900
ISBN (Print)9798350310917
Publication statusPublished - 26 Feb 2024

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813


  • backhaul
  • free space optical communication
  • hermite gaussian
  • machine learning
  • 6G
  • 5G


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