Virtualization Framework and VCG Based Resource Block Allocation Scheme for LTE Virtualization

Gao, Lvyang; Pei, Li; Pan, Zhiwen; Liu, Nan; You, Xiaohu

doi:10.1109/vtcspring.2016.7504529

Cited by 24 publications

(13 citation statements)

References 12 publications

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“…These works [41]- [43] were better in performance, but the approaches burdened the central controller because each slice must allocate resources individually. In [44], the authors proposed the virtualization framework for resource block allocation to its users using the auction-based game method but did not consider spectrum efficiency with D2D and edge computing. [45] discussed two cases of slicing; one is based on QoS to create dedicated slices depending on different services, and the second on infrastructure sharing in which the resource sharing is among the multi-tenants.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Nadeem¹,

Amin

Loo

et al. 2021

IEEE Access

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Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

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Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Nadeem¹,

Amin

Loo

et al. 2021

IEEE Access

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“…, K { } are randomly located in the coverage area of the base station (BS). e allocation round is one transmission time interval (TTI) [25]. During each TTI, the total bandwidth of the system is divided into N subbandwidth B on average and its corresponding subcarrier sets are I n � 1, 2, .…”

Section: System Modelmentioning

confidence: 99%

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

Zhang

Wang

et al. 2020

Wireless Communications and Mobile Computing

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is paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users' requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. en, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell-Hestenes-Rockafellar method and the branch and bound method, obtaining the optimal solution. e simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users' requirements change of two slices and get better spectral efficiency performance. methods to meet the different requirements of services with multiple logical networks. It has been a key enabler for 5G to accommodate a variety of services in a flexible manner [3]. erefore, it is critical to study multiservice problems in the virtual radio access network (RAN) by network slicing, especially different service requirements in various scenarios, such as enhanced mobile broadband (eMBB), ultrareliable low latency communication (URLLC), and massive machine-type communication (mMTC) [4,5]. e network slicing architecture consists of a core network (CN) and RAN slicing. Since the research of network slicing in the CN has been relatively mature and our research focuses on RAN slicing, the work of CN slicing is briefly introduced. For instance, in [6], the authors investigated how to combine fog node and network slicing of CN to safely access remote service data while ensuring low

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“…The design of pricing scheme is important for achieving incentive compatibility in the auction with which each bidder will always bid truthfully. The VCG [22] scheme achieves the incentive compatibility which have been proved which is only compatible with exact algorithms.…”

Section: Solving the Wdp In The Upper Levelmentioning

confidence: 99%

Hierarchical Combinatorial Auction in Computing Resource Allocation for Mobile Blockchain

Zhu

2020

Wireless Communications and Mobile Computing

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The mobile blockchain has been recognized as an emerging solution to address the security and privacy issues in a mobile application system. The mining process in mobile blockchain requires high computing resources which could overwhelm that which mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computing services to miners in mobile blockchain. Note that the resources of MESs are also limited; MESs could further request resources from the cloud computing server (CCS). Accordingly, the issue of hierarchical computing resource allocation arises. In this paper, we first consider a simple case with single-seller multiple buyers and a hierarchical single-seller multibuyer combinatorial auction model is proposed to solve this problem, based on which efficient and truthful frameworks are provided. We then extend the model to consider multiple CCSPs and propose a hierarchical multiple-seller multiple-buyer combinatorial auction model. For both models, the winner determination problems are formulated and computationally tractable algorithms are proposed. Also, pricing schemes are proposed to ensure the property of incentive compatibility and individual rationality. Finally, we evaluate the proposed schemes via simulations.

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Virtualization Framework and VCG Based Resource Block Allocation Scheme for LTE Virtualization

Cited by 24 publications

References 12 publications

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

Hierarchical Combinatorial Auction in Computing Resource Allocation for Mobile Blockchain

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