Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems

Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems

A Master of Science thesis in Electrical Engineering by Omar ElSakka entitled, “Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems”, submitted in April 2026. Thesis advisor is Dr. Mahmoud H. Ismail. Soft copy is available (Thesis, Completion Certificate, Approv...

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Bibliographic Details
Main Author: ElSakka, Omar (author)
Format: doctoralThesis
Published: 2026
Subjects:
Cell-free architecture
Grant-Free Random Access (GFRA)
Massive MIMO (mMIMO)
User Activity Detection (UAD)
Covariance-Based Detection
Sparse Bayesian Learning (SBL)
Riemannian Distances
Fisher Information Matrix (FIM)
K-Means
CatBoost
XGBoost
Akaike Information Criterion (AIC)
Minimum Description Length (MDL)
Machine Learning
5G Networks
Online Access:https://hdl.handle.net/11073/33525
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