Optimal Hybrid Quantum-Classical Convolutional Neural Network for Efficient 5G Network Slicing

Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supporting stringent requirements in terms of latency and bandwidth demands; as such, it must be highly dynamic and flexible. Network slicing is a key enabler technology that can provide dynamic and flexible characteristics to 5G network architecture. A network slice (NS) can be defined as a partition of network and IT resources, that is, network links and nodes capacity dedicated to a specific set of service demands. As a result, different NSs can coexist over the same physical infrastructure network and can be used to dynamically and flexibly deploy the aforementioned 5G services. However, to efficiently implement NSs with different requirements, communication service providers (CSPs) that own the physical infrastructure network must adopt sophisticated techniques for admission control and resource allocation of NSs. In this paper, we present a novel framework for admission control and resource allocation of 5G NSs in metro-core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) algorithm called Advantage Actor Critic (A2C), which performs admission control, i.e. it is capable of learning which slice to admit based on the availability of the physical network resources. Then, given the diversity of requirements for each 5G service, we propose different resource allocation algorithms based on integer linear programming (ILP) and heuristics to treat each service accordingly. Results show that our proposed framework can increase the number of admitted NSs with respect to the case in which the admission control is disabled by improving the resource allocation performance.

5G network supports end-to-end logically isolated networks in the form of network slices, catering to the needs of users of various primary network services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC). Mobile Virtual Network Operators (MVNO)s often face challenges in achieving strong slice isolation and High Availability per slice during overload and scaling situations as the 5G network uses a shared environment for slices with multiple domains, especially considering a variety of services and devices. In this paper, we propose a novel Self Optimizing Network Slicing framework (SONS) leveraging Self Organizing Network by building it as an autonomous slice system in 5G network slicing management for efficient slice sharing and isolation. Precisely, we formulate a system model with Probabilistic Graphical Model (PGM) based Markov Network, building it as an Artificial Intelligence based learning framework. We propose Slice Belief Propagation based algorithms and Deep Learning based Long Short Term Memory (LSTM) methods to aid in serving user requests and reconfiguration of self optimizing slice. Our experiments on the proposed SONS framework shows improvement in serving higher number of users with uninterrupted connectivity by 80% in eMBB, 35% in uRLLC, and 52% in mMTC when compared to standard slice deployments, while handling the worst case of peak traffic in the control plane of 5G Core network.

5G Stand Alone (SA) networks are in the process of implementation, as the today's progress of the main business services to migrate to the 5G new services communication (enhanced Mobile Broadband - eMBB, Ultra Reliable Low Latency Communications - URLLC, massive Machine Type Communications - mMTC) is estimated to slowly increase. There have been identified some key aspects responsible for the novel 5G communication adoption process, such as the complexity of the services deployment and the clear understanding of the huge potential of the technology that can further support the 5G vertical's stakeholders. This paper is representing the work of the EU funded project VITAL-5G in deploying 5G Stand Alone 3GPP Rel.16 testbeds, with enhanced network and services capabilities and 5G resources available to be offered to industries vertical's customers. The 5G solution of the testbed design is covering several aspects of the future 5G network implementation, such as services management and orchestration, automation of resources allocation, 5G network slicing (Radio Access Network, Core and Transport) and user traffic prioritization according to the service slice needs, eMBB and URLLC. An important aspect is the availability of the entire 5G ecosystem to be offered to the 5G developers and 3 rd parties for advanced and extensive trials such as Innovative Network Application (N etApps) implementations. By abstracting the complexity of underlying 5G infrastructure, reducing the time of service creation and deployment and optimizing the 5G resource usage, N etApps is a key enabler of 5G adoption.

Mobile Network Operator (MNO)s avail the benefit of providing an isolated service with the network slice in a variety of forms like enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). However, they face challenges in handling situations of overload, congestion, and scaling with the arrival of unexpected control plane User Service Requests (USRs) leading to the dropping of USRs, and hence disturbing the slice’s High Availability (HA). This paper presents a novel High Availability supportive self Reliant NEtwork Slicing System (HARNESS) for 5G Core, powered by intelligent and autonomous Self Organizing Network (SON) paradigm. In HARNESS, we propose algorithms to intelligently schedule and serve the significant portion of control plane USRs for both delay tolerant and delay sensitive slices, to ensure their uninterrupted HA service provision. Along with efficient resource utilization, the HARNESS shows improvements over the traditional HA provisioning methods, in preventing the occluding of crucial control plane USRs by 60% and reducing the average response time for these USRs by 50%. We developed the HARNESS framework in a 5G test-bed system using eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) mechanism for coherent and optimized end-to-end working of it. With this additional gear, HARNESS productively contributes to achieving 3.2% better slice service HA in the minimal two nodes active/active cluster configuration.

Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

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

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.

Fifth-Generation (5G) communication systems in-tend to meet stringent quality of service requirements such as reliable communication, low latency, providing high data rates with security constraints. The need for programmable solutions to enable the provision of services depending on these requirements can be addressed through Network Slicing to be deployed in 5G networks. In this paper, by simulating realistic user and base station data, artificial intelligence and machine learning techniques are explored for associating users with different types of communication slices. Instead of dealing with a snapshot of the environment, an evolving real-time scenario is adopted where hand overs between cells are taken into account. Neural network-based and random forest-based learning models were developed and tested in the placement of the users to Massive Internet of Things (MIoT), Ultra-Reliable Low-Latency Communications (URLLC), Vehicle to Everything (V2X), and enhanced Mobile Broadband (eMBB) slices.

To satisfy tight latency constraints, ultra–reliable low latency communications (URLLC) traffic is scheduled by overlapping the on–going enhanced mobile broad band (eMBB) transmissions (i.e., puncturing approach), which causes eMBB users unprecedented rate loss and hence degraded quality–of–service (QoS). To tackle this issue, this letter proposes to achieve QoS tradeoff between eMBB and URLLC in 5G networks. We jointly optimize bandwidth allocation and overlapping positions of URLLC users’ traffic with deep deterministic policy gradient algorithm observing channel variations and URLLC traffic arrivals. Simulation results show that the proposed system–wide tradeoff method achieves the best tradeoff performance.

The next-generation mobile communication network (5G) is a heterogeneous network, and it has the added advantage in the wireless communication field. Users will feel uninterrupted communication over the 5G network. It required a higher bandwidth in order to achieve a higher data rate. 5G is classified into three categories such as ultra-reliable low latency communication (URLLC), massive machine-type communication (mMTC), and enhance mobile broadband (eMBB) by the International Telecommunication Union (ITU). 5G will provide the higher data rate (Gbps), low latency, enhance quality of service (QoS), low energy consumption at a low cost per transmission, better spectral efficiency (SE), energy efficiency (EE), quality of service (QoS), improved throughput and better user experience. There will be so many challenges to achieve the above-mentioned factors. The main challenges are to reduce Interference, latency, power consumption, and enhance data rate. The paper highlights the different issues and challenges of 5G and compare different existing methodologies for mitigating these challenges.Keywords5GHeterogeneous networkMillimeter waveCoverage areaInterferenceLatencySpectral efficiencyEnergy efficiencyLow power consumption

Network slicing now delivers enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) over a common 5G core, yet deciding which slice requests to admit, where to place each virtual network function (VNF), and how to route and protect traffic forms an NP-hard mixed-integer problem. We first cast those decisions in a compact 0–1/mixed-integer programme whose feasible set Ω unifies admission, placement, latency, and capacity constraints. That formulation provides a common yardstick for four solution lines reviewed here: (i) exact models solved by integer linear programming (ILP); (ii) fast constructive heuristics; (iii) evolutionary and neighbourhood-search meta-heuristics; and (iv) learning-driven approaches built on deep- and graph-reinforcement learning (RL). Evidence from more than thirty benchmark studies reveals a clear pattern: ILP remains optimal up to roughly fifty nodes but scales poorly; greedy or multi-stage heuristics retain 85–95% of ILP revenue while running in near-linear time; well-tuned meta-heuristics cut the gap to under eight per cent on thousand-node cores; and trained RL agents issue millisecond decisions only a few points shy of meta-heuristic quality. Two recent two-edge-connected embedding schemes are included as illustrative case studies. Open challenges—hybrid matheuristic workflows, energy-aware objectives, reproducible failure-rich benchmarks, and sub-millisecond control-plane inference—are outlined as priorities for dependable slice management in 5G and beyond. 5G Core Network, Network Slicing, Integer-Linear Programming, Evolutionary Meta-Heuristics, Deep-Reinforcement Learning, Two-Edge-Connected Protection

With the advent of 5G technology, we are witnessing the development of increasingly bandwidth-hungry network applications, such as enhanced mobile broadband, massive machine-type communications and ultra-reliable low-latency communications. Software Defined Networking (SDN), Network Function Virtualization (NFV) and Network Slicing (NS) are gaining momentum not only in research but also in IT industry representing the drivers of 5G. NS is an approach to network operations allowing the partition of a physical topology into multiple independent virtual networks, called network slices (or slices). Within a single slice, a set of Service Function Chains (SFCs) is defined and the network resources, e.g. bandwidth, can be provisioned dynamically on demand according to specific Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Traditional schemes for network resources provisioning based on static policies may lead to poor resource utilization and suffer from scalability issues. In this article, we investigate the application of Reinforcement Learning (RL) for performing dynamic SFC resources allocation in NFV-SDN enabled metro-core optical networks. RL allows to build a self-learning system able to solve highly complex problems by employing RL agents to learn policies from an evolving network environment. In particular, we build an RL system able to optimize the resources allocation of SFCs in a multi-layer network (packet over flexi-grid optical layer). The RL agent decides if and when to reconfigure the SFCs, given state of the network and historical traffic traces. Numerical simulations show significant advantages of our RL-based optimization over rule-based optimization design.

Efficient spectrum and resource allocation remains a fundamental challenge in fifth generation (5G) wireless networks due to heterogeneous traffic requirements, spectrum scarcity, and dynamic interference conditions. Conventional scheduling and optimization techniques struggle to meet the stringent quality of service (QoS) demands of enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). This paper presents an improved deep reinforcement learning (DRL)-based optimization framework for adaptive spectrum and resource allocation in 5G networks. The proposed model integrates Deep Q-Network (DQN), Double DQN (DDQN), and DDQN with Prioritized Experience Replay (PER) to enhance convergence speed, stability, and scalability. A realistic simulation environment combining ray-traced channel data and packet-level traffic modeling was developed to evaluate system performance. Results demonstrate that the proposed DRL framework achieves up to 7% throughput improvement over proportional fair scheduling, reduces average latency by 10–12%, and lowers URLLC violation rates by approximately 15% under high-interference and heterogeneous traffic scenarios. These findings confirm the feasibility of DRL as a practical and scalable solution for real-time 5G resource management.

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.