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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/33525 |
| الوسوم: |
إضافة وسم
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| _version_ | 1870676404250607616 |
|---|---|
| author | ElSakka, Omar |
| author_facet | ElSakka, Omar |
| author_role | author |
| dc.contributor.none.fl_str_mv | Ibrahim, Mahmoud |
| dc.creator.none.fl_str_mv | ElSakka, Omar |
| dc.date.none.fl_str_mv | 2026-06-22T08:19:03Z 2026-06-22T08:19:03Z 2026-04 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2026.06 https://hdl.handle.net/11073/33525 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Master of Science in Electrical Engineering (MSEE) |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | 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, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_8ecb699766f80acfe451da62bae3ddd1 |
| identifier_str_mv | 35.232-2026.06 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/33525 |
| publishDate | 2026 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO SystemsElSakka, OmarCell-free architectureGrant-Free Random Access (GFRA)Massive MIMO (mMIMO)User Activity Detection (UAD)Covariance-Based DetectionSparse Bayesian Learning (SBL)Riemannian DistancesFisher Information Matrix (FIM)K-MeansCatBoostXGBoostAkaike Information Criterion (AIC)Minimum Description Length (MDL)Machine Learning5G NetworksA 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, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE)Ibrahim, Mahmoud2026-06-22T08:19:03Z2026-06-22T08:19:03Z2026-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2026.06https://hdl.handle.net/11073/33525en_USMaster of Science in Electrical Engineering (MSEE)oai:repository.aus.edu:11073/335252026-06-23T06:56:32Z |
| spellingShingle | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems ElSakka, Omar 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 |
| status_str | publishedVersion |
| title | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| title_full | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| title_fullStr | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| title_full_unstemmed | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| title_short | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| title_sort | Manifold-Based Machine Learning for Activity Detection in Grant-Free Massive MIMO Systems |
| topic | 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 |
| url | https://hdl.handle.net/11073/33525 |