Resource Allocation for Ultra-Dense Networks: A Survey, Some Research Issues and Challenges

Teng, Yinglei; Liu, Mengting; Yu, F. Richard; Leung, Victor C. M.; Song, Mei; Zhang, Yong

doi:10.1109/comst.2018.2867268

Cited by 154 publications

(103 citation statements)

References 280 publications

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“…In addition to the above benefits, there exist several challenges in H-CRAN MEC systems that can be induced by colocation of MEC and H-CRAN, e.g., deployment scenarios design. In the following, the major challenges of H-CRAN MEC systems are discussed [51], [208].…”

Section: B Motivations and Challengesmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Section: B Motivations and Challengesmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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“…f ∈ F, and n ∈ N . As such, M f APs in cluster f constitute a generalized APG, denoted by a set G f , to serve user n by concurrently transmitting independent signals in a user-centric way 4 , for G f ⊂ M. We wish to remark that the APs in generalized APG G f also belong to cluster f . 1 We would like to mention that our proposed optimization framework for joint resource allocation and user association is conducted within the considered time duration, which can be interpreted as a specific time slot or a period of time.…”

Section: Network Modelmentioning

confidence: 99%

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming,

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

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A coupling of wireless access via non-orthogonal multiple access (NOMA) and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, the ultra-dense deployment of radio access points in macrocell and the user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of the sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Furthermore, we solve each subproblem through the Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.

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“…3 Noticing that a detailed discussion on the clustering method is beyond the scope of this work. 4 We should pay more attention to the difference between the AP cluster and the APG in this work. An AP cluster is referred to as the result of the task of classifying all the APs into a specific disjoint part according to their spatial location relations.…”

Section: Network Modelmentioning

confidence: 99%

“…Instead of relying on a tower-mounted macro base station (MBS) with high transmit power in macrocell sending signals to a large number of user equipments (UEs), e.g., 0.2 UEs/m 2 , UDNs deploy tens or hundreds more of low-powered radio access points (APs) with smaller coverage areas to coherently provide wireless access service for those users. As such, the ultra-dense deployment of APs has potentials to bring multiple benefits, e.g., enlarged cell coverage, improved spatial reuse of wireless resources, enhanced performance gains, etc [4,5].In spite of being advantageous, such an increasing density of APs with dense cell coverage, e.g., 10 3 APs/km 2 or more, results in the complex distribution of APs in UDNs and even possible overlapped coverage for the users. Therefore, simply using the traditional cell-centric architecture poses extra challenges on network planning and design for UDNs, e.g., complicated resource management, severe inter-cell interference, large signalling overhead, etc.…”

mentioning

confidence: 99%

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

View full text Add to dashboard Cite

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.<br>

show abstract

Resource Allocation for Ultra-Dense Networks: A Survey, Some Research Issues and Challenges

Cited by 154 publications

References 280 publications

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming,

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming

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