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Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks
The proliferation of cellular networks over the past two decades has encouraged the expansion of their use in many modern applications. These applications involve the use of data traffic of different quality of service (QoS) requirements. Some of these requirements are quite stringent such as in the...
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2021
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| access_status_str | Open Access |
| author | Nomeir, Mohamed |
| author_browse | Nomeir, Mohamed |
| author_facet | Nomeir, Mohamed |
| author_sort | Nomeir, Mohamed |
| collection | Thesis |
| description | The proliferation of cellular networks over the past two decades has encouraged the expansion of their use in many modern applications. These applications involve the use of data traffic of different quality of service (QoS) requirements. Some of these requirements are quite stringent such as in the case of critical Internet of Things (IoT) health care, military and homeland security applications. This situation resulted in imposing a variety of resource allocation requirements on the cellular network operation in a simultaneous manner.
In this thesis, we consider the challenging problem of mixed-traffic resource allocation, or scheduling, in cellular networks. We focus our attention on 5G network as the most recent version currently being deployed worldwide. In this regard, there are generally two, separate, scheduling problems in communication systems, namely, the down-link (DL) scheduling and the up-link (UL) scheduling. Each of these problems has separate requirements, even if they both share some similarities. The DL focuses on scheduling the already received packets to the intended receivers and informing the receivers with enough information to receive the data correctly. This kind of scheduling is completely implemented by and controlled at the base station of the system. On the other hand, the UL problem focuses on providing enough resources to user devices to send their data, when they have any. In this thesis, we consider the problem of uplink scheduling in 5G networks for mixed traffic that includes Ultra-Reliable Low and Latency Communications (URLLC) devices and enhanced Mobile Broad-Band (eMBB) users. Each of these types has different requirements and therefore a different mathematical model based on the scheduling technique. There are three main scheduling techniques to be considered in this case, namely, the grant-based (GB), semi-persistent, and grant-free (GF) techniques. Each of these scheduling techniques is suitable for a certain type of traffic and has its own mathematical model that describes the associated traffic behavior. Furthermore, there are three different techniques used in grant-free scheduling, namely, the reactive scheme, the k-repetitions scheme and the proactive scheme. It has been concluded, in this study, that the grant-based scheduling is the best scheme for the eMBB traffic while the grant-free scheduling is best suitable for the URLLC traffic. For this purpose, we devise a mathematical model for the GF services using the k-repetitions Hybrid Automatic Repeat reQuest (HARQ) as the first model to define such traffic in a single cell. In addition, the GB scheduling model for eMBB traffic is adapted to fit our problem. We formulate the scheduling problem as a mixed-integer non-linear programming optimization problem. This type of problem is, in general, a complex problem due to its combinatorial nature. We introduce a complete system model that includes GF and GB subsystems. We introduce a novel mixed scheduler that combines the advantages of two well-known schedulers in the literature. We then introduce novel machine-learning based scheduling algorithms and evaluate them in comparison to some well-known algorithms in the literature in addition to the optimal bound that we also derive in this study. The results show that the proposed algorithms produce near-optimal results in real-time. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2762 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:51.500Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2762 Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks Nomeir, Mohamed The proliferation of cellular networks over the past two decades has encouraged the expansion of their use in many modern applications. These applications involve the use of data traffic of different quality of service (QoS) requirements. Some of these requirements are quite stringent such as in the case of critical Internet of Things (IoT) health care, military and homeland security applications. This situation resulted in imposing a variety of resource allocation requirements on the cellular network operation in a simultaneous manner. In this thesis, we consider the challenging problem of mixed-traffic resource allocation, or scheduling, in cellular networks. We focus our attention on 5G network as the most recent version currently being deployed worldwide. In this regard, there are generally two, separate, scheduling problems in communication systems, namely, the down-link (DL) scheduling and the up-link (UL) scheduling. Each of these problems has separate requirements, even if they both share some similarities. The DL focuses on scheduling the already received packets to the intended receivers and informing the receivers with enough information to receive the data correctly. This kind of scheduling is completely implemented by and controlled at the base station of the system. On the other hand, the UL problem focuses on providing enough resources to user devices to send their data, when they have any. In this thesis, we consider the problem of uplink scheduling in 5G networks for mixed traffic that includes Ultra-Reliable Low and Latency Communications (URLLC) devices and enhanced Mobile Broad-Band (eMBB) users. Each of these types has different requirements and therefore a different mathematical model based on the scheduling technique. There are three main scheduling techniques to be considered in this case, namely, the grant-based (GB), semi-persistent, and grant-free (GF) techniques. Each of these scheduling techniques is suitable for a certain type of traffic and has its own mathematical model that describes the associated traffic behavior. Furthermore, there are three different techniques used in grant-free scheduling, namely, the reactive scheme, the k-repetitions scheme and the proactive scheme. It has been concluded, in this study, that the grant-based scheduling is the best scheme for the eMBB traffic while the grant-free scheduling is best suitable for the URLLC traffic. For this purpose, we devise a mathematical model for the GF services using the k-repetitions Hybrid Automatic Repeat reQuest (HARQ) as the first model to define such traffic in a single cell. In addition, the GB scheduling model for eMBB traffic is adapted to fit our problem. We formulate the scheduling problem as a mixed-integer non-linear programming optimization problem. This type of problem is, in general, a complex problem due to its combinatorial nature. We introduce a complete system model that includes GF and GB subsystems. We introduce a novel mixed scheduler that combines the advantages of two well-known schedulers in the literature. We then introduce novel machine-learning based scheduling algorithms and evaluate them in comparison to some well-known algorithms in the literature in addition to the optimal bound that we also derive in this study. The results show that the proposed algorithms produce near-optimal results in real-time. 2021-12-27T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1730 https://fount.aucegypt.edu/context/etds/article/2762/viewcontent/Nomeir_Thesis_Final.pdf https://fount.aucegypt.edu/context/etds/article/2762/filename/0/type/additional/viewcontent/IRB_MohamedNomeir.pdf https://fount.aucegypt.edu/context/etds/article/2762/filename/1/type/additional/viewcontent/ApprovalSheet_MohamedNomeir.pdf https://fount.aucegypt.edu/context/etds/article/2762/filename/2/type/additional/viewcontent/Turnitin_MohamedNomeir.pdf Theses and Dissertations AUC Knowledge Fountain Uplink scheduling URLLC eMBB GB scheduling GF scheduling ML RL NN Systems and Communications |
| spellingShingle | Uplink scheduling URLLC eMBB GB scheduling GF scheduling ML RL NN Systems and Communications Nomeir, Mohamed Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks |
| title | Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks |
| title_full | Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks |
| title_fullStr | Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks |
| title_full_unstemmed | Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks |
| title_short | Machine Learning Based Critical Resource Allocation in Mixed-Traffic Cellular Networks |
| title_sort | machine learning based critical resource allocation in mixed traffic cellular networks |
| topic | Uplink scheduling URLLC eMBB GB scheduling GF scheduling ML RL NN Systems and Communications |
| url | https://fount.aucegypt.edu/etds/1730 https://fount.aucegypt.edu/context/etds/article/2762/viewcontent/Nomeir_Thesis_Final.pdf https://fount.aucegypt.edu/context/etds/article/2762/filename/0/type/additional/viewcontent/IRB_MohamedNomeir.pdf https://fount.aucegypt.edu/context/etds/article/2762/filename/1/type/additional/viewcontent/ApprovalSheet_MohamedNomeir.pdf https://fount.aucegypt.edu/context/etds/article/2762/filename/2/type/additional/viewcontent/Turnitin_MohamedNomeir.pdf |
| work_keys_str_mv | AT nomeirmohamed machinelearningbasedcriticalresourceallocationinmixedtrafficcellularnetworks |