QoS-Aware Energy-Efficient MicroBase Station Deployment for 5G-Enabled HetNets

Guo, Wanying; Koo, Jahwan; Siddiqui, Isma Farah; Qureshi, Nawab Muhammad Faseeh; Shin, Dong Ryeol

doi:10.1016/j.jksuci.2022.11.004

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…In addition, the significant investment in new energy will also bring volatility and instability to the system. The work angle stability of global power systems, including wind and flexible DC transmission lines, are studied in (6) (8). Understanding the system's characteristics through the system trajectory for complex nonlinear systems is most intuitive.…”

Section: Introductionmentioning

confidence: 99%

AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

Guo

Qureshi

Jarwar

et al. 2023

Sustainable Energy Technologies and Assessments

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Section: Introductionmentioning

confidence: 99%

AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

Guo

Qureshi

Jarwar

et al. 2023

Sustainable Energy Technologies and Assessments

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“…Network operators have taken proactive steps to address these difficulties by gradually adopting the deployment of micro base stations (µBS). Integrating these µBS amplifies network coverage and enhances overall capacity, thereby ensuring a more robust and reliable communications network [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Slicing of mmWave Micro Base Stations for 5G and Beyond

Fayad,

Cinkler

2023

J-NaNA

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5G and beyond 5G mobile networks are expected to cater to diverse needs by efficiently allocating network resources based on demand. Network slicing is a fundamental approach that involves segregating and allocating network resources distinctly to a group of users based on their individual needs, and it is widely recognized as an essential concept that caters to various requirements. Allocating such slices will encounter conflicting requests, and effectively implementing network slicing presents multiple challenges. Effective network slicing necessitates efficient management of priority levels among diverse slices. Network slicing necessitates efficient management of priority levels across various slices, specifically focusing on three distinctive categories: Ultra-Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). This paper proposes an optimization framework utilizing a Mixed Integer Linear Program (MILP) to allocate network resources for multiple slices efficiently. Our framework aims to maximize user satisfaction while ensuring that the specific requirements of each slice are met. We categorize the slices into three priority levels: the URLLC slice holds the highest priority, followed by the eMBB slice, and finally, the mMTC slice receives the least priority. By leveraging our proposed MILP-based approach, we dynamically assign network resources to different slices, considering their priority levels. This allocation strategy enables us to optimize resource utilization and effectively meet the diverse demands of users across various slices. Our framework provides a balance between meeting the stringent requirements of the URLLC slice, delivering high-quality services to the eMBB slice, and accommodating the massive connectivity needs of the mMTC slice.

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QoS-Aware Energy-Efficient MicroBase Station Deployment for 5G-Enabled HetNets

Cited by 2 publications

References 34 publications

AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

Optimal Slicing of mmWave Micro Base Stations for 5G and Beyond

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