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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	ElSakka, Omar (author)
التنسيق:	doctoralThesis
منشور في:	2026
الموضوعات:	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
الوصول للمادة أونلاين:	https://hdl.handle.net/11073/33525
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الانترنت

https://hdl.handle.net/11073/33525

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

الانترنت

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