Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

A key enabler for Industry 4.0 is Fifth Generation Wireless Specifications (5G), within which network slicing is a promising technique to ensure customized quality of service for specific end-user groups in industrial scenarios. Massive Multiple Input Multiple Output (MIMO) plays a significant role in 5G but network slicing for massive MIMO has not yet been addressed. In this paper, we propose a network slicing scheme for a 5G Radio Access Network (RAN) with massive MIMO technology. Our simulations show that it is feasible to provide guaranteed performance in terms of high reliability, low latency, and a large number of connections by deploying our proposed scheme at the mMedium Access Control (MAC) layer of a massive MIMO base station.

Network slicing is a fundamental feature of 5G systems that facilitates the provision of particular system behaviours adapted to specific service/application domains on top of a common network infrastructure. A network slice is in general composed by a core network slice and a Radio Access Network (RAN) slice. The realization of RAN slices is particularly challenging because it requires configuring and operating traffic differentiation and protection mechanisms to simultaneously deliver multiple and diverse RAN behaviors over a given pool of radio resources. In this context, this paper proposes to characterize the behavior of a RAN slice through the specification of a set of control parameters that are used to dictate the operation of the packet scheduling function at Layer 2 and the radio admission control function at Layer 3. An evaluation of the suitability of these parameters for achieving efficient radio resource sharing and isolation between RAN slices is presented when configuring a network for supporting a slice with multiple enhanced Mobile BroadBand services and another slice for providing Mission Critical services. The analysis reveals the different impact of the Layer 3 and Layer 2 parameters for isolating services of different slices depending on whether they require guaranteed or non-guaranteed bit rates.

5G radio access network (RAN) slicing is gaining momentum in finding applications in a wide range of domains, especially those requiring high-speed data transmissions such as space-air-ground integrated networks (i.e., drone-based systems). A key challenge in building a robust RAN slicing to support these applications is, therefore, designing a RAN slicing (RS)-configuration scheme that can utilize information such as resource availability in substrate networks as well as the interdependent relationships among slices to map (embed) virtual network functions (VNFs) onto live substrate nodes. With such motivation, we propose a machine-learning-powered RAN slicing scheme that aims to accommodate maximum numbers of slices (a set of connected VNFs) within a given request set. We present a deep reinforcement scheme that is called Deep Allocation Agent (DAA). In short, DAA utilizes an empirically designed deep neural network that observes the current states of the substrate network and the requested slices to schedule the slices of which VNFs are then mapped to substrate nodes using an optimization algorithm. DAA is trained towards the goal of maximizing the number of accommodated slices in the given set by using an explicitly designed reward function. Our experiment study shows that, on average, DAA is able to maintain a rate of successfully routed slices above 80% in a resource-limited substrate network, and about 60% in extreme conditions, i.e., the available resources are much less than the demands.

5G network is anticipated to meet the challenging requirements of mobile traffic in the 2020's, which are characterized by super high data rate, low latency, high mobility, high energy efficiency, and high traffic density. This paper provides an overview of China Mobile's 5G vision and potential solutions. Targeting a paradigm shift to user-centric network operation from the traditional cell-centric operation, 5G radio access network (RAN) design considerations are presented, including RAN restructure, Turbo charged edge, core network (CN) and RAN function repartition, and network slice as a service. Adaptive multiple connections in the user-centric operation is further investigated, where the decoupled downlink and uplink, decoupled control and data, and adaptive multiple connections provide sufficient means to achieve a 5G network with "no more cells." Software-defined air interface (SDAI) is presented under a unified framework, in which the frame structure, waveform, multiple access, duplex mode, and antenna configuration can be adaptively configured. New paradigm of 5G network featuring user-centric network (UCN) and SDAI is needed to meet the diverse yet extremely stringent requirements across the broad scope of 5G scenarios.

Network slicing is one of the key enabling techniques for 5G, allowing Mobile Network Operators (MNOs) to support services with diverging requirements on top of their infrastructure. The MNOs should be able to offer network slices as a service and provide customisable and independent virtual networks to verticals. The slicing of an end-to-end (E2E) mobile network is divided into Core Network (CN) slicing, and Radio Access Network (RAN) slicing. In this paper, we assess the requirements for using radio hypervisors to enable RAN as a Service (RANaaS). We evaluate the current state-of-the-art on radio virtualisation with respect to these requirements and identify the missing features. Then, we present the eXtensible Virtualisation Layer (XVL), a software layer that provides the missing functionality for enabling RANaaS and can be added on top of existing radio hypervisors. We outline XVL's architecture and design choices, as well as evaluate its performance in terms of the delay to provision virtual radios, the delay introduced to forward IQ samples, and the computational overhead. Our results show that XVL enables leveraging existing radio hypervisors to support RANaaS.

In a 5G Radio Access Network (RAN), different features are offered as solutions to serve traffic with diverse characteristics and requirements, including flexible numerology, (non-)pre-emptive mini-slot based scheduling and network slicing. In this paper, we present an extensive simulation-based assessment of the relative merit of these distinct 5G features in the context of a smart city environment. We further derive the optimal feature combination and associated configuration which best handles the services related to the smart city environment given their performance requirements. The obtained insights confirm the commonly argued potential of slicing, emphasizing that the optimal configuration of the slice-specific numerology depends not only on the nature of the handled services but also on the selected RAN features. Among these features, non-preemptive mini-slot based scheduling and idle resource sharing reveal significant performance potential.

The proliferation of mobile demands and increasingly multifarious services pose challenges to both the radio access networks (RANs) and the core network (CN). Unfortunately, the traditional networks can hardly meet the increasing requirements. Software-defined network (SDN) and network virtualization (NV) are considered as innovative paradigms to stress these problems. By extending SDN to wireless networks, software-defined wireless network (SDWN) consisting of both RANs and CN can be obtained. In this paper, the RANs and CN of SDWN are virtualized to virtual access points (VAPs) and network slices respectively based on NV technology. Considering the system scenario that a SDWN consists of overlapping RANs, an optimal joint VAP and network slice selection scheme is proposed. To characterize the complicated transmission performance of VAPs and network slices, the concepts of utility function and network calculus are introduced, and the joint utility of VAPs and network slices is formulated based on which the optimal VAP and network slice combination corresponding to the maximal joint utility can be selected. The simulation results demonstrate the efficiency of the proposed scheme.

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities into independent components. Network slicing for O-RAN can significantly improve performance. Therefore, an advanced resource allocation solution for network slicing in O-RAN is proposed in this study by applying Reinforcement Learning (RL). This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices. Increasing the throughput for certain network slicing can also benefit the end users with a higher average data rate, peak rate, or shorter transmission time. The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.

Network slicing enabled by fifth generation (5G) systems has the potential to satisfy diversified service requirements from different vertical industries. As a typical vertical industry, smart distribution grid poses new challenges to communication networks. This paper investigates the behavior of network slicing for smart grid applications in 5G radio access networks with heterogeneous traffic. To facilitate network slicing in such a network, we employ different 5G radio access numerologies for two traffic classes which have distinct radio resource and quality of service requirements. Three multi-dimensional Markov models are developed to assess the transient performance of network slicing for resource allocation with and without traffic priority. Through analysis and simulations, we investigate the effects of smart grid protection and control traffic on other types of parallel traffic sessions as well as on radio resource utilization.

5G emerges with ultra-dense deployments of small cell networks to address the location specific service demand of various vertical sectors. While the development of technical solutions for network densification is progressing, less attention has been paid to local operator models and location specific service offerings. Efficient local service delivery promoting innovation and competition calls for opening of the mobile market for new entrants to operate local radio access networks (RAN) and 3rd parties to integrate their services into the networks. This paper applies the recently proposed micro operator concept to local ultra-dense networks where different stakeholders can become micro operators and deploy their small cell RAN for tailored service delivery. We depict the resulting high-level architecture and enabling techniques focusing on network slicing and spectrum micro licensing. While network slicing is well addressed in 5G research from the mobile network operator (MNO) view for providing an end-to-end connection for 3rd parties, there is little work on how the ultra-dense small cell RAN could be operated by another stakeholder. Micro operators could offer the RAN or end-to-end network slice that comprises of network elements belonging to different stakeholders with the help of local spectrum micro licensing with quality guarantees.

With the advent of 5G era, network slicing has received a great deal of attention as a means to support a variety of wireless services in a flexible manner. Network slicing is a technique to divide a single physical resource network into multiple slices supporting independent services. In beyond 5G (B5G) systems, the main goal of network slicing is to assign the physical resource blocks (RBs) such that the quality of service (QoS) requirements of eMBB, URLLC, and mMTC services are satisfied. Since the goal of each service category is dearly distinct and the computational burden caused by the increased number of time slots is huge, it is in general very difficult to assign RB properly. In this paper, we propose a deep reinforcement learning (DRL)-based network slicing technique to find out the resource allocation policy maximizing the long-term throughput while satisfying the QoS requirements in the B5G systems. Key ingredient of the proposed technique is to reduce the action space by eliminating undesirable actions that cannot satisfy the QoS requirements. Numerical results demonstrate that the proposed technique is effective in maximizing the long-term throughput and handling the coexistence of use cases in the B5G environments.

With the advent of the 5G era, due to the limited network resources and methods before, it cannot be guaranteed that all services can be carried out. In the 5G era, network services are not limited to mobile phones and computers but support the normal operation of equipment in all walks of life. There are more and more scenarios and more and more complex scenarios, and more convenient and fast methods are needed to assist network services. In order to better perform network offloading of the business, make the business more refined, and assist the better development of 5G network technology, this article proposes 5G network slicing: methods to support blockchain and reinforcement learning, aiming to improve the efficiency of network services. The research results of the article show the following: (1) In the model testing stage, the research results on the variation of the delay with the number of slices show that the delay increases with the increase of the number of slices, but the blockchain + reinforcement learning method has the lowest delay. The minimum delay can be maintained. When the number of slices is 3, the delay is 155 ms. (2) The comparison of the latency of different types of slices shows that the latency of 5G network slicing is lower than that of 4G, 3G, and 2G network slicing, and the minimum latency of 5G network slicing using blockchain and reinforcement learning is only 15 ms. (3) In the detection of system reliability, reliability decreases as the number of users increases because reliability is related to time delay. The greater the transmission delay, the lower the reliability. The reliability of supporting blockchain + reinforcement learning method is the highest, with a reliability of 0.95. (4) Through the resource utilization experiment of different slices, it can be known that the method of blockchain + reinforcement learning has the highest resource utilization. The resource utilization rate of the four slices under the blockchain + reinforcement learning method is all above 0.8 and the highest is 1. (5) Through the simulation test of the experiment, the results show that the average receiving throughput of video stream 1 is higher than that of video stream 2, IOT devices and mobile devices, and the average cumulative receiving throughput under the blockchain + reinforcement learning method. The highest is 1450 kbps. The average QOE of video stream 1 is higher than that of video stream 2, IOT devices and mobile devices, and the average QOE is the highest under the blockchain + reinforcement learning method, reaching 0.83.

The 5th generation of mobile communications is expected to provide a network suitable for any service requirements using a technology termed network slicing. In network slicing, network resources are divided and are allocated to slices for services. Radio access network slicing is essential for end-to-end network slicing. The state of the radio access network changes from moment to moment and automatic control of network slicing is necessary to respond to service requirements in real time. Automatic control is applied using deep reinforcement learning that can learn optimal control by repeating trial and error. In this paper, we propose a method to allocate radio resources that satisfy the service requirements regardless of the number of slices using deep reinforcement learning. The proposed method flexibly controls multiple slices by observing the state of one slice and calling a model that controls one slice multiple times. The proposed method was evaluated by simulation using a scenario in which the number of slices changes with time. The results of the evaluation show that variations in slice numbers achieve almost completely satisfying of slice requirements without being affected by other slices.

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