Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation.

Sagor MIK, Rahman MZ, Mandal P

DOI: 10.1371/journal.pone.0353163
Published: 2026
Container: PloS one
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0353163,
  title = {Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation.},
  author = {Sagor MIK and Rahman MZ and Mandal P},
  year = {2026},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0353163},
  url = {https://doi.org/10.1371/journal.pone.0353163}
}

RIS

TY  - JOUR
TI  - Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation.
AU  - Sagor MIK
AU  - Rahman MZ
AU  - Mandal P
PY  - 2026
JO  - PloS one
DO  - 10.1371/journal.pone.0353163
UR  - https://doi.org/10.1371/journal.pone.0353163
ER  -

APA

MIK, S., MZ, R., & P, M. (2026). Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation.. PloS one. https://doi.org/10.1371/journal.pone.0353163

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pubmed · retrieved 2026-09-25T03:56:06.289Z