Multi-Service Resource Allocation in Future Network With Wireless Virtualization

Li, Letian; Deng, Na; Ren, Wei; Kou, Baohua; Zhou, Wuyang; Yu, Shui

doi:10.1109/access.2018.2871506

Cited by 17 publications

(24 citation statements)

References 28 publications

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“…We solve the two problems P 1 and P 2 that are still the convex optimization problem. Combining Algorithm 1, we get u 1 , N (1) k , p (1) k and greater than 0.005 sec. When there is a higher delay requirement of D k,max ≤ 0.003 sec, the URLLC slice is more reliable when using the JPSA algorithm, and its overall reliability is higher than the EPA and ESA algorithms.…”

Section: End Ifmentioning

confidence: 96%

“…e mobile Internet and the IoT tend to be the main forces to drive the development of mobile communication in the future network. In particular, the equipment of massive IoT that connected to sensors such as humidity sensors and remote industrial robots needs to provide divergent services [1]. For the multiservice requirement of vertical industries in the 5th generation (5G), the one-size-fits-all design concept is not viable anymore, and it is a promising approach to meet the requirement that slicing one physical network into several logical networks according to different demands [2].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: End Ifmentioning

confidence: 96%

Section: Introductionmentioning

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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“…(2) We assume the packet whose delay is larger than its maximum time delay will be dropped. The delay utility should decrease rapidly when delay increases and approaches the maximum time delay [31].…”

Section: ) Utility Design and Dynamic Time-slot Allocation Strategy mentioning

confidence: 99%

A Hierarchical Approach to Resource Allocation in Extensible Multi-Layer LEO-MSS

Deng

Zhou

2020

IEEE Access

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Low earth orbit mobile satellite system (LEO-MSS) is the major system to provide communication support for mobile terminals beyond the coverage of terrestrial communication systems. However, the quick movement of LEO satellites and current single-layer system architecture impose restrictions on the capability to provide satisfactory service quality, especially for the remote and non-land regions with high traffic requirement. To tackle this problem, high-altitude platforms (HAPs) and terrestrial relays (TRs) are introduced to cover hot-spot regions, and the current single-layer system becomes an LEO-HAP multi-layer access network. Under this setup, we propose a hierarchical resource allocation approach to circumvent the complex management caused by the intricate relationships among different layers. Specifically, to maximize the throughputs, we propose a dynamic multi-beam joint resource optimization method in LEO-ground downlinks based on the predicted movement of LEO satellites. Afterwards, we propose the dynamic resource optimization method of HAP-ground downlinks when LEO satellites and HAPs share the same spectrum. To solve these problems, we use the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to find the optimal solutions. Numerical results show that the proposed architecture outperforms current LEO-MSS in terms of average capacity. In addition, the proposed optimization methods increase the throughputs of LEO-ground downlinks and HAP-ground downlinks with an acceptable complexity. INDEX TERMS Radio resource allocation, multi-beam satellite, multi-layer satellite network, LEO mobile satellite system, space-air-ground integrated network.

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“…• The packet with a larger delay than the upper delay limit should be dropped. The delay utility should decrease rapidly as delay increases and approaches the upper delay limit [37].…”

Section: B Utility Designmentioning

confidence: 99%

“…There are 200 services on-going at the first timeslot. New services arrival processes are modeled as Poisson distributed with average arrival rate λ. Packets of ongoing services arrive every 100 ms on average and the packet sizes are normally distributed with the means of 500k bits [37]. The duration time of services follows the exponential distribution with mean θ = 1 minute.…”

Section: A System Parametersmentioning

confidence: 99%

Forecast Based Handover in an Extensible Multi-Layer LEO Mobile Satellite System

Zhou

2020

IEEE Access

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Low earth orbit mobile satellite system (LEO-MSS) is the major system to provide communication support for the regions beyond the coverage of terrestrial network systems. However, passive handover happens frequently caused by the quick movement of LEO satellites in LEO-MSS. It not only causes the waste of radio resource, but also makes it hard to guarantee the quality of service (QoS), especially for user groups in hot-spot regions. To tackle this problem, we propose an extensible multi-layer network architecture to reduce the handover rate, especially group handover rate by introducing high-altitude platforms (HAPs) and terrestrial relays (TRs) to this system. We then propose a multi-layer handover management framework and also design different handover procedures based on handover forecast for different kinds of handovers according to the proposed architecture and framework to reduce handover delay and signalling cost. Furthermore, we propose a dynamic handover optimization to reduce the dropping probability and guarantee the QoS of mobile terminals. Numerical results show that the proposed architecture reduces group handovers significantly. The proposed handover procedures also provide better performance on delay and signalling cost compared with traditional handover protocols. With the proposed dynamic handover optimization, the proposed handover procedures provide better performance on dropping probability and throughput. The proposed dynamic handover optimization has an excellent performance on dropping probability while guaranteeing the QoS of mobile terminals.

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Multi-Service Resource Allocation in Future Network With Wireless Virtualization

Cited by 17 publications

References 28 publications

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

A Hierarchical Approach to Resource Allocation in Extensible Multi-Layer LEO-MSS

Forecast Based Handover in an Extensible Multi-Layer LEO Mobile Satellite System

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