Radio Access Network Slicing Research Articles

Efficient and Fair Multi-Resource Allocation in Dynamic Fog Radio Access Network Slicing

Future wireless networks should meet heterogeneous service requirements of diverse applications, including interactive multimedia, augmented reality, and autonomous driving. The fog radio access network (Fog-RAN) is a novel architecture that enables efficient and flexible allocation of network resources to end users. However, guaranteeing application-specific service requirements while maximizing resource utilization is an open challenge in Fog-RANs. This article proposes a multiresource Fog-RAN slicing scheme that maximizes network resource utilization and satisfies important economic properties: Pareto-optimality, envy freeness, and sharing incentive. The proposed solution considers both heterogeneous resources (i.e., bandwidth, storage, and computing) and the different service levels defined in 5G networks. Accordingly, a two-level resource scheduling mechanism is devised to jointly allocate Fog-RAN resources to slices in two stages: 1) a broker allocates resources to slices at fog nodes over a given time window and 2) a slice hypervisor then allocates slice-specific resources at each fog node to users with a much shorter time scale. An extensive evaluation based on real-world data sets demonstrates that the proposed solution significantly increases the monetary gain of service providers, namely, by 32%–60% compared to the state of the art, including dynamic hierarchical resource allocation and dynamic slicing with proportional allocation.

Learning From Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing

Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing. The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network management. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing. We first define a hierarchical architecture for joint resource allocation. Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement learning (QDTRL) and action selection-based deep transfer reinforcement learning (ADTRL). In the proposed schemes, learner agents utilize expert agents’ knowledge to improve their performance on current tasks. The proposed algorithms are compared with both the model-free exploration bonus deep Q-learning (EB-DQN) and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL-based method presents 21.4% lower delay for Ultra Reliable Low Latency Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile Broad Band (eMBB) slice, while achieving significantly faster convergence than EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are observed with respect to PPF-TTL.

Toward Radio Access Network Slicing Enforcement in Multi-cell 5G System

The proliferation of sophisticated applications and services comes with diverse performance requirements. The 5G cellular network is advocated to support this diversity through an end-to-end network slicing. Even-though the slicing is not a novel concept, its implementation in the RAN still remains challenging. In this article, we aim to enforce the real time 5G slicing from radio resources perspective in a multi-cell system. For that, an exact optimization model is proposed. Due to its high convergence time, heuristics are developed and evaluated with regard to the optimal model. Results are promising, as two heuristics are highly enforcing the real time RAN slicing.

Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing.

Multiservice cellular in Radio Access Network (RAN) Slicing has recently attained huge interest in enhancing isolation and flexibility. However, RAN slicing in heterogeneous networks (HetNet) architecture is not adequately explored. This study proposes a pairing-network slicing (NS) approach for Multiservice RAN that cares about quality of service (QoS), baseband resources, capacities of wireless fronthaul and backhaul links, and isolation. This intriguing approach helps address the increased need for mobile network traffic produced by a range of devices with various QoS requirements, including improved dependability, ultra-reliability low-latency communications (uRLLC), and enhanced broadband Mobile Services (eMBB). Our study displays a unique RAN slicing framework for user equipment (UE) for joint user-association. Multicell non-orthogonal multiple access (NOMA)-based resource allocation across 5G HetNet under successive interference cancelation (SIC) is seen to achieve the best performance. Joint user-slice pairing and association are optimization problems to maximize eMBB UE data rates while fulfilling uRLLC latency and reliability criteria. This is accomplished by guaranteeing the inter- and intra-isolation property of slicing to eliminate interferences between eMBB and uRLLC slices. We presented the UE-slice association (U-S. A) algorithm as a one-to-many matching game to create a stable connection between UE and one of the base stations (BSs). Next, we use the UE-slice pairing (U-S. P) algorithm to find stable uRLLC-eMBB pairs that coexist on the same spectrum. Numerical findings and performance analyses of the submitted association and pairing technique show they can all be RAN slicing criteria. We prove that the proposed algorithm optimizes system throughput while decreasing uRLLC latency by associating and pairing every uRLLC user in mini slots.

A tenant-driven slicing enforcement scheme based on Pervasive Intelligence in the Radio Access Network

In Beyond 5G mobile networks, the network slicing paradigm offers the possibility of sharing the network infrastructure among different tenants: the tenant declares communication service requirements and the Infrastructure Provider configures (potentially on-demand) the corresponding network slice instances. The management of network slicing in the core network has been deeply investigated in the current scientific literature. On the contrary, handling network slicing in the Radio Access Network still represents an open and very challenging research topic, mostly due to the unpredictable variability of the wireless channel, network dynamics and heterogeneity, slice isolation, as well as different Quality of Service requirements of various services. In order to achieve an important step forward in this direction, this paper proposes a tenant-driven Radio Access Network slicing enforcement scheme based on Pervasive Intelligence. The proposed approach grounds its roots in the Pay for What You Get paradigm: it promises to avoid the radio resources over-provisioning while saving bandwidth. To achieve these goals, Artificial Intelligence mechanisms are innovatively and pervasively integrated into some key functionalities of both Infrastructure Provider and tenants. On the one hand, the Infrastructure Provider exploits a Deep Learning scheme (i.e., convolutional autoencoder) to compress the information on network resources and connectivity and share the actual (but hidden through compression) network status with the tenants. On the other hand, each tenant implements a Deep Reinforcement Learning algorithm (i.e., Deep Deterministic Policy Gradient) to dynamically adapt bandwidth requests according to its own users’ requirements. The outcomes of this algorithm are then used by the Infrastructure Provider to effectively enforce network slicing at the Radio Access Network level. Computer simulations investigate the proposed approach in a realistic scenario, which jointly embraces enhanced Mobile BroadBand, Advanced Driver Assistant Systems, and best-effort applications. Obtained results demonstrate the effectiveness of the proposal against conventional resource allocation methods.

Deep reinforcement learning based flexible preamble allocation for RAN slicing in 5G networks

One of the most difficult challenges in radio access network slicing occurs in the connection establishment phase where multiple devices use a common random access channel in order to gain access to the network. It is now very well known that random access channel congestion is a serious issue in case of sporadic arrival of machine-to-machine nodes and may result in a significant delay for all nodes. Hence, random access channel resources are also needed to be allocated to different services to enable random access network slicing. In the random access channel procedure, the nodes transmit a selected preamble from a predefined set of preambles. If multiple nodes transmit the same preamble at the same random access channel opportunity, a collision occurs at the eNodeB. To isolate the one service class from others during this phase, one approach is to allocate different preamble subsets to different service classes. This research proposes an adaptive preamble subset allocation method using deep reinforcement learning. The proposed method can distribute preambles to different service classes according to their priority providing virtual isolation for service classes. The results indicate that the proposed mechanism can quickly adapt the preamble allocation according to the changing traffic demands of service classes.

Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services

Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions.

Using 5G in smart cities: A systematic mapping study

• 32 studies published in 27 venues are finally included. • The most discussed scenario is transportation. • There are three types of architecture of 5G-enabled smart cities. • The most mentioned 5G-related technology is radio access technologies. • Challenges are complex context, challenging requirements, and 5G network development. 5G is the fifth generation wireless network, with a set of characteristics, such as high bandwidth and data rates, massive connectivity, broad coverage, and low latency. The scenarios of using 5G include enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and ultra-Reliable and Low-Latency Communications (uRLLC). 5G is expected to support a wide variety of applications, such as city management, healthcare, transportation, agriculture, and energy management. In this paper, we conducted a systematic mapping study that covers the literature published between January 2012 and December 2019 regarding using 5G in smart cities. The scenarios, architecture, technologies, challenges, and lessons learned of using 5G in smart cities are summarized and further analyzed based on 32 selected studies, and the results are that: (1) The studies are distributed over 27 publication venues. 17 studies report results based on academic studies and 13 studies use demonstration or toy examples. Only 2 studies report using 5G in smart cities based on industrial studies. 16 studies include assumptions of 5G network design or smart city scenarios. (2) The most discussed smart city scenario is transportation, followed by public safety, healthcare, city tourism, entertainment, and education. (3) 28 studies propose and/or discuss the architecture of 5G-enabled smart cities, containing smart city architecture (treating 5G as a component), 5G network architecture in smart cities, and business architecture of using 5G in smart cities. (4) The most mentioned 5G-related technologies are radio access technologies, network slicing, and edge computing. (5) Challenges are mainly about complex context, challenging requirements, and network development of using 5G in smart cities. (6) Most of the lessons learned identified are benefits regarding 5G itself or the proposed 5G-related methods in smart cities. This work provides a reflection of the past eight years of the state of the art on using 5G in smart cities, which can benefit both researchers and practitioners in this field.

Blockchain-Enabled Resource Trading and Deep Reinforcement Learning-Based Autonomous RAN Slicing in 5G

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.

User-Based Quality of Service Aware Multi-Cell Radio Access Network Slicing

5G radio access network (RAN) slicing envisions a solution to flexibly deploy heterogeneous services as slices sharing the same infrastructure. However, this level of flexibility renders slice isolation challenging, mainly due to the stochastic nature of wireless resources. In the state-of-the-art, RAN slicing algorithm’s efficiency with respect to slice isolation is related to the ability of meeting individual slice requirements. However, mostly an aggregated slice performance guarantee is considered instead of per user guarantees. Hence, state-of-the-art approaches might not always provide the satisfaction of all users within a slice. Indeed, our results demonstrate that if user requirements within a slice are not included in the RAN slicing algorithm, the per user quality-of-service (QoS) may not be fulfilled. In this paper, we investigate the definition of slice isolation as the ability to satisfy individual users’ throughput within slices, in a frequency selective, multi-cell wireless scenario with focus on maximizing slices’ throughput. Our problem is tackled with a Lyapunov optimization approach, which proves to always achieve slice isolation. Our results show that our solution does not only achieve 100% user QoS guarantees compared to 50% achieved in the state-of-the-art, but also doubles the throughput with increasing number of BSs.

GAN-Based Pareto Optimization for Self-Healing of Radio Access Network Slices

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands and provide profitable business models for future mobile networks. In RAN slicing architecture, self-healing is an important functional module to minimize the impact of network failings on the performance of RAN slices. However, self-healing of burgeoning sliced RAN is vastly different from that of traditional RAN and has been rarely investigated. In this paper, we address the Self-healing of RAN Slice (SRANS) problem by modeling it as a Pareto optimization problem with the aim of maximizing the self-healing utilities of individual RAN slices. To deal with the weakness of diversity maintenance in traditional Pareto optimization methods, we propose a Generative Adversarial Network (GAN) based Pareto Optimization (GPO) framework. Specifically, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Evolutionary Algorithm (EA), where the insufficiency of diversity maintenance in EAs is effectively overcome. Furthermore, we theoretically prove that GPO framework is guaranteed to converge to the optimal Pareto solution set. Numerical results demonstrate that the convergence of proposed GPO framework can be expedited by enhancing the diversity of solution sets in solving the SRANS problem. Compared with traditional schemes, GPO can achieve significant performance gain in terms of the utilities and isolation level of repaired RAN slices.

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

One of the key foundations of 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

Energy-Optimal End-to-End Network Slicing in Cloud-Based Architecture

Network slicing is a promising technology for realizing the vision of supporting a wide range of services with diverse and heterogeneous service requirements. With network slicing, the network is partitioned into multiple individual dedicated networks tailored and customized for specific services. However, this causes extra energy consumption to reserve resources for each slice. On the other hand, minimizing the energy consumption in radio access network (RAN) may result in increasing the energy consumption in the cloud and the fronthaul due to higher required processing and data transport. Therefore, the energy should be evaluated from an end-to-end perspective. In this study, we address the problem of minimizing the end-to-end energy consumption of a network with network slicing by jointly reserving communication and computation resources among slices. First, we propose an end-to-end delay and energy model for each slice. We take into account the delays and energy consumption of the radio site, midhaul/fronthaul transport, and the cloud site in an Ethernet-based cloud RAN (C-RAN). Then, we formulate a non-convex optimization problem to minimize the total energy consumption of the network by jointly allocating transmission bandwidth and processing resources in the digital unit pool of the cloud, respectively, to each slice. The constraints of the optimization problem are the total delay requirement of each slice, the maximum allowable bandwidth at each radio unit, the maximum rate limitation of the Ethernet links, and the total processing limit of the cloud. To solve the problem optimally, we transform it into a convex quadratic programming problem. The simulation results show that end-to-end network slicing can decrease the total energy consumption of the network compared to only RAN slicing. We also investigate the impact of the 5G numerology on the allocated resources to each slice and the total energy consumption. We show that using mixed numerology depending on the slice type, we can interplay between delay and energy consumption for each slice.

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization–management and orchestration (NFV–MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(iii)</i> to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks (RAN) slicing, service provisioning for slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association with base station (BS) and logical association with NS should be considered. In other words, the user-BS-NS three layer association relationship should be addressed in provisioning tailored service for diversified use cases with various quality of service (QoS) requirements. Therefore, service provisioning in RAN slicing becomes an essential yet challenging issue for 5G and beyond systems. In this paper, we propose a unified framework for service provisioning in RAN slicing with aim of maximizing resource utilization while guaranteeing QoS of users. The framework consists of two steps. The first step is to identify a set of slice users whose QoS can be satisfied simultaneously; while the second step performs joint slice association and bandwidth allocation with aim to minimize bandwidth consumption. Numerical results show that in typical scenarios, our proposed service provisioning framework can achieve significant performance gain in terms of the number of serving users and wireless bandwidth utilization compared with traditional schemes.

Deep reinforcement learning‐aided RAN slicing enforcement supporting latency sensitive services in B5G networks

Edge computing and artificial intelligence promise to turn future mobile networks into service‐ and radio‐aware infrastructures, able to address the requirements of upcoming latency‐sensitive applications. For instance, they can be used to dynamically and optimally manage the Radio Access Network Slicing. However, this is a challenging goal, due to the mostly unpredictably nature of the wireless channel. This paper presents a novel architecture using Deep Reinforcement Learning at the network edge for addressing Radio Access Network Slicing and Radio Resource Management. By considering the autonomous‐driving use‐case, computer simulations demonstrate the effectiveness of our proposal against baseline methodologies.

Online Convex Optimization for Efficient and Robust Inter-Slice Radio Resource Management

Radio access network (RAN) slicing is one of the key technologies in 5G and beyond mobile networks, where multiple logical subnets, i.e., RAN slices, are allowed to run on top of the same physical infrastructure so as to provide slice-specific services. Due to the dynamic environments of wireless networks and the diverse requirements of RAN slices, inter-slice radio resource management (IS-RRM) has become a highly challenging task in RAN slicing. In this paper, we propose a novel online convex optimization (OCO) framework for IS-RRM, which directly learns the instant resource allocation from the data revealed by previous allocations, such that sophisticated modeling and parameterization can be avoided in highly complicated and dynamic wireless environments. Specifically, an online IS-RRM scheme that employs multiple expert-algorithms running in parallel is proposed to keep track of the environmental changes and adjust the resource allocation accordingly. Both theoretical analysis and simulation results show that our proposed scheme can guarantee long-term performance comparable to the optimal strategies given in hindsight.

Trade-off analysis between delay and throughput of RAN slicing for smart grid

Emerging power services with massive connections and real-time control have different requirements on delay, rate, reliability, etc. In order to meet the diversified requirements of power services, network slicing can provide guarantee for the wide application of 5G in power. The current research on network slicing adapting power services is still in its infancy. This paper proposes a trade-off analysis method for radio access network (RAN) slice delay and throughput based on dynamic intelligent resource allocation to meet the different QoS requirements of different services in smart grid scenarios. Firstly, clustering analysis is used to classify the communication requirements of smart grid services. A throughput demand model for enhanced mobile broadband (eMBB) slice users based on importance and a delay constraint model for ultra-reliable and low latency communication (uRLLC) slice users are constructed, and a throughput model for slice users based on cell capacity limitation is constructed. Then, the problem model of maximizing the throughput of the eMBB slice by reasonably allocating resource blocks and power under the premise of ensuring the delay of the uRLLC slice is established. The Lyapunov drift plus penalty function method is used to convert the above problem into a tractable form, and then the problem is solved by the active set method. Simulation results show that the different QoS requirements of smart grid services will significantly affect the actual queue length of the uRLLC slice as well as the throughput of the eMBB slice.

Coordinated 5G Network Slicing: How Constructive Interference Can Boost Network Throughput

Radio access network (RAN) slicing is a virtualization technology that partitions radio resources into multiple autonomous virtual networks. Since RAN slicing can be tailored to provide diverse performance requirements, it will be pivotal to achieve the high-throughput and low-latency communications that next-generation (5G) systems have long yearned for. To this end, effective RAN slicing algorithms must (i) partition radio resources so as to leverage coordination among multiple base stations and thus boost network throughput; and (ii) reduce interference across different slices to guarantee slice isolation and avoid performance degradation. The ultimate goal of this paper is to design RAN slicing algorithms that address the above two requirements. First, we show that the RAN slicing problem can be formulated as a 0-1 Quadratic Programming problem, and we prove its NP-hardness. Second, we propose an optimal solution for small-scale 5G network deployments, and we present three approximation algorithms to make the optimization problem tractable when the network size increases. We first analyze the performance of our algorithms through simulations, and then demonstrate their performance through experiments on a standard-compliant LTE testbed with 2 base stations and 6 smartphones. Our results show that not only do our algorithms efficiently partition RAN resources, but also improve network throughput by 27% and increase by 2× the signal-to-interference-plus-noise ratio.

5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues

SummaryNetwork slicing (NS) presents the key enabler of cellular network improvements. It allows enhancing the performance of diverse requirements supported for verticals industries. The concept of NS was carefully studied over the previous few years, and the primary operational principles were developed. However, there is an important need for more investigations on studying NS to enable further development. This article offers a deep study related to the NS principle, the recent standardization process for Third Generation Partnership Project and Fifth Generation Public Private Partnership, the diverse broad use cases, NS key concepts, NS architectures, and NS management and orchestration. Besides, it discusses radio access network slicing and sharing, the algorithms, the projects, and the NS practical experience and practices. Finally, this article proposes and highlights a possible solution to several open research issues.