Dynamic Network Slicing for Multitenant Heterogeneous Cloud Radio Access Networks

Lee, Ying Loong; Loo, Jonathan; Chuah, Teong Chee; Wang, Li-Chun

doi:10.1109/twc.2017.2789294

Cited by 129 publications

(92 citation statements)

References 46 publications

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“…In this section, we analyze the resource allocation of RAN slicing from the perspective of single service and multiservice. Regarding the single service of RAN slicing, the researchers focused on the price of slice resources [3,8,[14][15][16] and the throughput [17,18]. In [3], wang et al studied resource pricing as a Stackelberg pricing game, in which network slice customers (NSCs) maximize their profit by adjusting their slice's resource requirement.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

“…In [3], wang et al studied resource pricing as a Stackelberg pricing game, in which network slice customers (NSCs) maximize their profit by adjusting their slice's resource requirement. Lee et al [18] considered the multitenant cellular network slicing, aiming to achieve a higher network throughput, fairness, and QoS performance.…”

Section: Related Workmentioning

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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“…Extensive efforts have been devoted to the enhancement of fronthaul capacity, including compression/quantization [9]- [11], quality of service (QoS) guarantee [12], [13], interference mitigation [14], [15], user/access link selection [16]- [18], as well as resource allocation and optimization [8], [19]- [21].…”

Section: A Related Workmentioning

confidence: 99%

“…The optimal minimum mean square error (MMSE) channel estimator [39] is given by (13) at the top of this page 1 , where Y r ∈ C M×Ur is the received signal matrix over the U r pilot symbols, and X…”

Section: B Signal Model Of Ud-to-rrumentioning

confidence: 99%

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Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

Zhang

Chen

Guo

et al. 2019

IEEE Trans. Veh. Technol.

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The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multipleinput multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33% compared with the one attained without optimizing power sharing factors.

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An efficient resource allocation to improve QoS of 5G slicing networks using general processor sharing‐based scheduling algorithm

AlQahtani

2019

Int J Communication

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Fifth generation (5G) slicing is an emerging technology for software-defined networking/network function virtualization-enabled mobile networks.Improving the utilization and throughput to meet the quality of service (QoS) requirements of 5G slicing is very important for the operators of mobile networks. With growing data traffic from different applications of numerous smart mobile devices having several QoS requirements, we expect networks to face problems of congestion and overload that prevent the effective functioning of a radio access network (RAN). This paper proposes a more effective packetbased scheduling scheme for data traffic by 5G slicing with two operation modes for improving the resource utilization of 5G cloud RAN and providing an efficient isolation of the 5G slices. These two operation modes are referred to as static sharing resource (SSR) scheme and dynamic sharing resources (DSR) scheme. The SSR scheme is a modified version of an existing method. The goal of this approach is to reallocate the shared available resources of 5G network fairly and maximize the utilization of bandwidth while protecting a 5G slice from overwhelming other 5G slices. Throughput and delays of the system model are also discussed to show its performance limits. On the basis of the simulation outcomes, we observed that the proposed DSR scheme outperforms the SSR scheme in terms of provided delay and throughput. In addition, the token bucket parameters together with the assigned capacity weight for each slice can be selected and configured based on the required QoS. Finally, a good estimate for the maximum delay bounds of the slices is provided by the derived theoretical delay bound.

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Dynamic Network Slicing for Multitenant Heterogeneous Cloud Radio Access Networks

Cited by 129 publications

References 46 publications

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

An efficient resource allocation to improve QoS of 5G slicing networks using general processor sharing‐based scheduling algorithm

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