Research on Intelligent Communication Scheduling System Based on Network Slices

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.

End-to-end network slicing in unmanned aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an ϵ-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (MaxSINR), and random algorithms.

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 has been proposed as an innovative concept to partition a physical network into different configurable slices, thereby allowing the network to meet the diverse requirements of expected services in the 5G era. Existing network-slicing schemes focus almost exclusively on the partitioning of the core network or the radio access network. However, to achieve greater flexibility and better resource utilization in service provisioning, the potential of network slicing in the 5G optical mobile fronthaul (MFH) network should be jointly exploited. To address this challenge, we propose a flexible hierarchical edge cloud architecture to enable 5G optical MFH network slicing. Additionally, a corresponding integrated network resource management scheme is proposed to support various quality-of-service (QoS) requirements and diverse resource requests of the network slices. Simulation results indicate that the proposed scheme is able to jointly allocate the bandwidth resources to network slices and realize cloud-computing offloading to meet various QoS requirements, and therefore reduce the fronthaul bandwidth burden.

Network slicing is one of the most promising enabling technologies for next generation mobile network (5G), which spans over all domains of the network, including data centers, core networks (CNs), transport networks, and radio access networks (RANs). As such wide range of technologies are involved, it is difficult to ensure an end-to-end (network) slice working stably all the time. First, for both CN and RAN slices, bugs may accidentally occur in virtual network functions(VNFs), which can invalidate the slice and trigger a recovery process. In addition, the traffic demands to be accommodated by a CN or RAN slice can be stochastic rather than deterministic parameters, and changes of traffic demands that are too drastic may trigger slice reconfiguration. In this chapter, we investigate a robust RAN slicing mechanism to address slice recovery and reconfiguration in a unified framework. A slice is constituted by a set of VNFs and links and remapping is a process of re-selecting VNFs and links to recover failures. As a baseline, we first formulate a recovery problem to solve remapping of a slice with deterministic traffic demands. Based on that, we propose two robust RAN slicing algorithms for slice recovery under stochastic demands. Numerical results reveal that the proposed robust RAN slicing algorithms can provide adjustable tolerance of traffic uncertainties, compared with the baseline algorithm. In addition, the tradeoff between the robustness of slices and the average load of links and the tradeoff between the robustness and the average recovery time of slices can be efficiently managed and controlled.

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 this chapter, we present a comprehensive end-to-end (E2E) network slicing framework for fifth-generation (5G) wireless and core networks and provide our vision on network slicing for beyond 5G (B5G) networks using artificial intelligence (AI) technologies. We first give an introduction on the 5G communication networks by specifying distinct 5G networking characteristics. As the key features of 5G pose challenges on quality-of-service (QoS)-oriented resource provisioning, a new concept of network slicing is presented, based on software-defined networking (SDN) and network function virtualization (NFV) technologies, to enhance the overall resource utilization while guaranteeing QoS isolation among services. We focus on developing specific network slicing solutions for both 5G wireless networks and core networks. In addition, the evolution of B5G networks is discussed in terms of a potential networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate the effectiveness of the proposed E2E network slicing solutions.Keywords5G communication networksNetwork slicingWireless networksRadio resource slicingCore networksComputing and transmission resource slicingBeyond 5G networksAI-assisted network slicingQoSE2E delay

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.

With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones.

The evolution of communication networks has witnessed a remarkable transformation with the advent of 5G technology. As the latest generation of cellular networks, 5G promises to revolutionize the way we connect, communicate, and interact with the digital world. This paper explores the architecture, management, and implications of 5G communication networks, shedding light on the key features, challenges, and opportunities presented by this transformative technology. At the core of 5G networks lies a sophisticated architecture designed to deliver unprecedented levels of speed, reliability, and connectivity. The 5G New Radio (NR) architecture encompasses three primary components: the User Equipment (UE), the Radio Access Network (RAN), and the Core Network (CN). These components work in tandem to facilitate seamless communication between user devices and the broader network infrastructure. One of the defining features of 5G networks is their ability to support significantly higher data speeds and lower latency compared to previous generations. With the potential to deliver data rates of several gigabits per second and latency as low as a few milliseconds, 5G opens the door to a plethora of innovative applications and services, ranging from immersive virtual reality experiences to real-time remote surgeries. Effective management of 5G networks is paramount to ensuring optimal performance, reliability, and security. Network management systems employ advanced algorithms for dynamic resource allocation, quality of service (QoS) optimization, and network slicing. Network slicing, in particular, enables operators to create virtual networks tailored to specific use cases, such as enhanced mobile broadband, massive IoT deployments, and mission-critical communications. However, the deployment and management of 5G networks pose several challenges and considerations. Infrastructure deployment requires substantial investment and coordination, while ensuring interoperability between different vendors' equipment and standards remains a pressing issue. Spectrum availability and allocation also play a critical role in determining the performance and capacity of 5G networks, necessitating careful regulatory oversight and spectrum management strategies. Despite these challenges, the implications of 5G technology are far-reaching and profound. From empowering smart cities and autonomous vehicles to enabling the widespread adoption of IoT devices and applications, 5G networks have the potential to reshape industries, drive innovation, and enhance the quality of life for billions of people around the world. 5G communication networks represent a paradigm shift in the way we connect and communicate. With their advanced architecture, dynamic management capabilities, and transformative potential, 5G networks are poised to unlock new opportunities, accelerate digital transformation, and usher in a new era of connectivity and innovation.

The evolution of communication networks has witnessed a remarkable transformation with the advent of 5G technology. As the latest generation of cellular networks, 5G promises to revolutionize the way we connect, communicate, and interact with the digital world. This paper explores the architecture, management, and implications of 5G communication networks, shedding light on the key features, challenges, and opportunities presented by this transformative technology. At the core of 5G networks lies a sophisticated architecture designed to deliver unprecedented levels of speed, reliability, and connectivity. The 5G New Radio (NR) architecture encompasses three primary components: the User Equipment (UE), the Radio Access Network (RAN), and the Core Network (CN). These components work in tandem to facilitate seamless communication between user devices and the broader network infrastructure. One of the defining features of 5G networks is their ability to support significantly higher data speeds and lower latency compared to previous generations. With the potential to deliver data rates of several gigabits per second and latency as low as a few milliseconds, 5G opens the door to a plethora of innovative applications and services, ranging from immersive virtual reality experiences to real-time remote surgeries. Effective management of 5G networks is paramount to ensuring optimal performance, reliability, and security. Network management systems employ advanced algorithms for dynamic resource allocation, quality of service (QoS) optimization, and network slicing. Network slicing, in particular, enables operators to create virtual networks tailored to specific use cases, such as enhanced mobile broadband, massive IoT deployments, and mission-critical communications. However, the deployment and management of 5G networks pose several challenges and considerations. Infrastructure deployment requires substantial investment and coordination, while ensuring interoperability between different vendors' equipment and standards remains a pressing issue. Spectrum availability and allocation also play a critical role in determining the performance and capacity of 5G networks, necessitating careful regulatory oversight and spectrum management strategies. Despite these challenges, the implications of 5G technology are far-reaching and profound. From empowering smart cities and autonomous vehicles to enabling the widespread adoption of IoT devices and applications, 5G networks have the potential to reshape industries, drive innovation, and enhance the quality of life for billions of people around the world. 5G communication networks represent a paradigm shift in the way we connect and communicate. With their advanced architecture, dynamic management capabilities, and transformative potential, 5G networks are poised to unlock new opportunities, accelerate digital transformation, and usher in a new era of connectivity and innovation.

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.

The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

The automobile industries are undergoing a technological revolution, which has facilitated the emergence of multiple Vehicle-to-Everything (V2X) applications with diverse Quality of Service (QoS) requirements. The conventional resource allocation methods are no longer applicable in V2X due to the diverse vehicular services with different QoS requirements in various practical scenarios. Therefore, this paper proposes a multi-service resource allocation strategy for V2X. Specifically, the problem of allocating resources for multiple V2X services is decoupled into two sub-problems, i.e., resource allocation between slices, and resource scheduling within slice. Then, a heuristic resource allocation algorithm is adopted to solve the resource allocation between slices sub-problem. For Real-Time (RT) and Non-Real Time (NRT) services in the vehicular network, specialized scheduling has been designed for the near-real-time Radio Access Network (RAN) Intelligent Controller and non-real-time RAN Intelligent Controller in the Open Radio Access Network (O-RAN) architecture, to meet the heterogeneous QoS service requirements of vehicles. Simulation results demonstrate that compared to the modified largest weighted delay first (M-LWDF) algorithm, the proposed resource allocation strategy can enhance the AoI of the proportional fairness (PF) services by 15%, and provide 7.39% transmission rate gain for NRT services.

Network slicing is considered a key feature of 5G and beyond cellular systems. It opens the door for new business models of mobile operators, enables new services, reduces costs with advanced infrastructure-sharing techniques, and improves heterogeneous traffic service. With slicing, the operators can tailor the network resources to the requirements of specific verticals, applications, and corresponding traffic types. To satisfy the heterogeneous quality of service (QoS) requirements of various slices, efficient virtualization and resource allocation algorithms are required. Such algorithms are especially crucial for the radio access network (RAN) because of the spectrum scarcity. This article develops DeSlice, a novel architecture for RAN slicing. DeSlice enables efficient real-time slicing algorithms that satisfy heterogeneous QoS requirements of the slices and improve the quality of experience for their end users. The article illustrates the advantages of DeSlice by considering the problem of the joint service of cloud VR, video, and web traffic. It develops the algorithms using DeSlice architecture and application-to-network communication. With simulations, it shows that, together, the architecture and the algorithms allow greatly improving the QoE for these traffics significantly.