Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach
The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every...
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2025-01-01
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| author | Suhare Solaiman |
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| description | The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every service has distinct quality of service (QoS) requirements, it is essential to ensure the seamless coexistence of these devices. The main difficulty is allocating network resources to maximize spectrum utilization while meeting mMTC’s massive connectivity demands and offering URLLC’s demand for ultra-reliable, low-latency communication. In this study, non-orthogonal multiple access (NOMA) network slicing is utilized to share radio resources among various services, thereby improving connectivity for large-scale device deployments. When these services exist in high-density NOMA environments characterized by high network congestion with radio resource sharing, the level of difficulty increases significantly. To address these issues, an optimization algorithm is proposed for subchannel assignment and power allocation in NOMA high-density networks for URLLC and mMTC devices. The solution adopts a Q-learning algorithm to optimize decision-making processes and ensure efficient resource sharing between URLLC and mMTC devices, while satisfying their distinct QoS requirements. Extensive simulations demonstrate that the proposed algorithm is flexible and scalable in dynamic scenarios, outperforming random and exhaustive search algorithms in high-density NOMA networks in terms of sum rates. The sum rate of the proposed algorithm increased by approximately 23.44% compared to that of the exhaustive search algorithm. |
| format | Article |
| id | doaj-art-651762d682e5452fb5dfcf8ed4eb32d4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-651762d682e5452fb5dfcf8ed4eb32d42025-02-11T00:01:16ZengIEEEIEEE Access2169-35362025-01-0113243232433510.1109/ACCESS.2025.353865410870268Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning ApproachSuhare Solaiman0https://orcid.org/0000-0002-1418-4715Department of Computer Science, College of Computer and Information Technology, Taif University, Taif, Saudi ArabiaThe growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every service has distinct quality of service (QoS) requirements, it is essential to ensure the seamless coexistence of these devices. The main difficulty is allocating network resources to maximize spectrum utilization while meeting mMTC’s massive connectivity demands and offering URLLC’s demand for ultra-reliable, low-latency communication. In this study, non-orthogonal multiple access (NOMA) network slicing is utilized to share radio resources among various services, thereby improving connectivity for large-scale device deployments. When these services exist in high-density NOMA environments characterized by high network congestion with radio resource sharing, the level of difficulty increases significantly. To address these issues, an optimization algorithm is proposed for subchannel assignment and power allocation in NOMA high-density networks for URLLC and mMTC devices. The solution adopts a Q-learning algorithm to optimize decision-making processes and ensure efficient resource sharing between URLLC and mMTC devices, while satisfying their distinct QoS requirements. Extensive simulations demonstrate that the proposed algorithm is flexible and scalable in dynamic scenarios, outperforming random and exhaustive search algorithms in high-density NOMA networks in terms of sum rates. The sum rate of the proposed algorithm increased by approximately 23.44% compared to that of the exhaustive search algorithm.https://ieeexplore.ieee.org/document/10870268/Network slicingNOMAQ-learning algorithmURLLCmMTC |
| spellingShingle | Suhare Solaiman Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach IEEE Access Network slicing NOMA Q-learning algorithm URLLC mMTC |
| title | Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach |
| title_full | Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach |
| title_fullStr | Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach |
| title_full_unstemmed | Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach |
| title_short | Optimizing Subchannel Assignment and Power Allocation for Network Slicing in High-Density NOMA Networks: A Q-Learning Approach |
| title_sort | optimizing subchannel assignment and power allocation for network slicing in high density noma networks a q learning approach |
| topic | Network slicing NOMA Q-learning algorithm URLLC mMTC |
| url | https://ieeexplore.ieee.org/document/10870268/ |
| work_keys_str_mv | AT suharesolaiman optimizingsubchannelassignmentandpowerallocationfornetworkslicinginhighdensitynomanetworksaqlearningapproach |