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Network slicing (NS) has emerged as a promising solution that enables network operators to slice network resources such as spectrum and bandwidth to adapt to different beyond 5G scenarios. This allows new operators to enter the market: the infrastructure provider (InP), who owns the infrastructure, and the mobile virtual network operator (MVNO), who may purchase a resource slice from the InP to provide a specific service to their end-users. To better deal with the resource allocation problem, efficient algorithms and methods have been done such as the auction model, bidding method, and game theory. This paper presents an upper-tier resource allocation based on game theory. This mechanism considers a single base station (BS) and multi MVNOs-users that share aggregated bandwidth radio access networks to maximize utilized BS resources. The proposed method takes both the bandwidth utilization of BS and the service requirements of MVNO users. Accordingly, the Game Theory solution takes two contradictory objectives: the InP aims to maximize its revenue while the MVNOs want to serve their users by paying the minimum amount. We prove that our proposal achieves an optimal solution from both InP and MVNOs' in terms of revenue and quality of service. [ABSTRACT FROM AUTHOR]

Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two‐layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi‐variational problem, indicating that the MPEC is solvable. Combining the two‐layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing. [ABSTRACT FROM AUTHOR]

Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5–2.7% higher than DQN and Double DQN, but the convergence is faster. [ABSTRACT FROM AUTHOR]

With centralized processing, cooperative radio, and cloud infrastructure, cloud radio access network (CRAN) has attracted much attention due to its flexibility in network operation and resource management. In this paper, we propose a pricing-based resource allocation strategy in virtualized CRANs, where one mobile network operator (MNO) owns the physical resource and multiple mobile virtual network operators (MVNOs) serve their users with resources leased from the MNO. The problem is naturally formulated as a bi-level optimization problem, where the upper level corresponds to revenue maximization of the MNO and the lower level corresponds to the utility-surplus maximization of the MVNOs. To solve the problem efficiently, we propose to solve the MVNO problem with a low-complexity algorithm, which scales well with the network size. Based on the analysis of the revenue of the MNO, we find that different utility function families result in distinct revenue trends with respect to the price, and correspondingly propose efficient algorithms to find the optimal price that maximizes the revenue. Through simulations, we demonstrate that the proposed algorithm for the MVNO problem can reduce the complexity from $\mathcal {O}(M^3)$ of interior point methods to $\mathcal {O}(M)$ , where $M$ is the number of users. Meanwhile, the simulation results show that the proposed pricing methods can find the optimal price in a few iterations. [ABSTRACT FROM AUTHOR]

This article investigates the slicing concept in the 5G RAN with the related challenges and research problems. The objective is to identify the plausible options for implementing the slicing concept at the RAN level by the mobile network operator to respond to the needs of verticals. We start by identifying the different slice granularity options, that is, how to define slices by combining customer and service needs. We then present how the 5G NR features can be used for facilitating slice implementation and provide typical configurations for different slice types from technology and RAN architecture perspectives. The main challenges for RAN slicing are then discussed, with special attention to the resource allocation problem between slices sharing the same spectrum band. We also investigate the multi-tenant slicing implementation in terms of the openness of the network to third parties, which is regarded as a key issue that may encourage vertical players to use operators' networks rather than deploying their own infrastructure. [ABSTRACT FROM AUTHOR]

With the development of smart mobile devices and various mobile applications, content-oriented service has become the most popular service which occupies network resources and results in high traffic load. In order to improve quality of experience in radio access network and reduce the OpEx and CapEx of operators, wireless network virtualization and network slicing come into the vision and are deemed as promising solutions to radio access networks to provide tailored services. Therefore, network slicing and optimization based on content-oriented service become a challenging research direction. In this paper, network slicing and resource optimization on content-oriented application in cache-enabled hybrid radio access network based on complex network are investigated. A Cooperative Network Slicing Framework Based on Content in RAN (CNSC-RAN) is presented. Based on CNSC-RAN, procedures of content-oriented static network slicing and dynamic slicing are proposed. Content-oriented slicing is modeled and analyzed which includes slicing on content cache resources and communication resources. In order to obtain the optimized resources sliced for each content, the optimization problem is formulated to minimize the average system cost to get the contents required by users. The problem is solved by a heuristic algorithm called CCSOA (Content-Centric Slicing Optimization Algorithm) in a dynamic content-oriented network slicing procedure enabling UEs with self-evicting contents. The performance of CCSOA is evaluated by performance metrics including hit rate, average cache occupation, average system cost, and request traffic reduction to macro cell base station comparing with CEE and ProbCache. Simulation results reveal the effectiveness of CCSOA. [ABSTRACT FROM AUTHOR]

Virtual radio access networks (RANs) is the candidate solution for 5G access networks, the concept of virtualised radio resources completing the virtual RAN paradigm. This paper proposes a new analytical model for the management of virtual radio resources in full heterogeneous networks. The estimation of network capacity and data rate allocation are the model's two main components. Based on the probability distribution of the signal-to-interference-plus-noise-ratio observed at the user terminal, the model leads to the probability distribution for the total network data rate. It considers different approaches for the estimation of the total network data rate, based on different channel qualities, i.e., optimistic, realistic and pessimistic. The second component uses the outcome of the first one in order to maximise the weighted data rate subject to the total network capacity, the SLAs (service level agreements) of Virtual Network Operators (VNOs), and fairness. The weights for services in the objective function of the resource allocation component enable the model to have prioritisation among services. The performance of the proposed model is evaluated in a practical heterogeneous access network. Results show an increase of 2.5 times in network capacity by implementing an access point at the centre of each cell of a cellular network. It is shown that the cellular network capacity itself can vary from 0.9 Gbps in the pessimistic approach up to 5.5 Gbps in the optimistic one. Finally, the isolation of service classes and VNOs by means of virtualisation of radio resources is clearly demonstrated. [ABSTRACT FROM AUTHOR]

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that integrates the advantages of cloud radio access networks and heterogeneous networks. In this paper, we study joint congestion control and resource optimization to explore the energy efficiency (EE)-guaranteed trade-off between throughput utility and delay performance in a downlink slotted H-CRAN. We formulate the considered problem as a stochastic optimization problem, which maximizes the utility of average throughput and maintains the network stability subject to the required EE constraint and transmit power consumption constraints by traffic admission control, user association, resource block allocation, and power allocation. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed and decomposed into three separate subproblems that can be concurrently solved at each slot. The third mixed-integer nonconvex subproblem is efficiently solved by utilizing the continuity relaxation of binary variables and the Lagrange dual decomposition method. Theoretical analysis shows that the proposal can quantitatively control the throughput–delay performance trade-off with the required EE performance. Simulation results consolidate the theoretical analysis and demonstrate the advantages of the proposal from the prospective of queue stability and power consumption. [ABSTRACT FROM PUBLISHER]

This article first analyzes the requirements and challenges in the future 5G mobile wireless network. Then the authors describe a new method for radio access technologies called virtual RATs. The proposed scheme can provide a flexible and tailored access network that can meet the requirements and overcome the challenges in mobile broadband networks. The authors also present a high level design of the overall architecture and protocol stack for virtual RATs. Two concepts, virtual RAT Types and interface sets, are introduced. Two essential features, flexible control/user (C/U) plane separation and coordination between virtual RATs, are also discussed. Examples are provided to show how to achieve a flexible and tailored access network for services by using virtual RATs. Finally, the capability of hardware to support implementation of virtual RATs is analyzed. [ABSTRACT FROM PUBLISHER]