Network Slicing Research Articles

Digital Twins-Empowered Secure Network Slice Access and Isolation for Consumer Healthcare Applications

Digital Twins-Empowered Secure Network Slice Access and Isolation for Consumer Healthcare Applications

Hierarchical Network Slicing for UAV-assisted Wireless Networks with Deployment Optimization

Hierarchical Network Slicing for UAV-assisted Wireless Networks with Deployment Optimization

A Multi-Level Deep RL-Based Network Slicing and Resource Management for O-RAN-Based 6G Cell-Free Networks

A Multi-Level Deep RL-Based Network Slicing and Resource Management for O-RAN-Based 6G Cell-Free Networks

DT-LNS: Digital Twin-Based Low-Risk Network Slicing Using Safe Reinforcement Learning

DT-LNS: Digital Twin-Based Low-Risk Network Slicing Using Safe Reinforcement Learning

Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (ORAN) Deployment

Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (ORAN) Deployment

Resource Management From Single-Domain 5G to End-to-End 6G Network Slicing: A Survey

Resource Management From Single-Domain 5G to End-to-End 6G Network Slicing: A Survey

Deep Customized Network Slicing and Efficient Routing for IoT Applications in B5G-Enabled Edge Computing Networks

Deep Customized Network Slicing and Efficient Routing for IoT Applications in B5G-Enabled Edge Computing Networks

AoIT-Empowered Associated Network Slicing: Resource Orchestration for Joint Monitoring

AoIT-Empowered Associated Network Slicing: Resource Orchestration for Joint Monitoring

Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks

Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks

Efficient resource allocation through CNN-game theory based network slicing recognition for next-generation networks

Fifth generation (5G) and sixth generation (6G) networks are examples of next-generation networks that need higher levels of safety, lower latency, and more capacity and dependability. Reconfigurable wireless connection slicing becomes essential for satisfying these sophisticated networks' requirements, enabling many network instances on the same hardware to improve Quality of Service (QoS). Nonetheless, the centrally managed resource allocation for network slicers presents difficulties, particularly as the quantity of User Equipment (UEs) increases. This puts pressure on Radio Resource Management (RRM) and makes slice customization more difficult. In order to address these issues, this study presents an organizational radio resource distribution architecture in which the neighborhood radio resource managers (LRRMs) receive sub channel allocations from the RRM in slices, and the LRRMs then distribute the assigned capabilities to the corresponding UEs. The suggested model, which runs in MATLAB, uses an original method called CNN-Game Theory to achieve an exceptional 98 % accuracy, outperforming CNN-LSTM, RNN, DeepCog, and DHOA by 29.27 %. This method combines ideas from game theory with neural network weight optimization to produce an improved model with increased efficiency and accuracy. Many experiments illustrate how effective this method is and how it can be used to improve different machine learning applications. Metrics like slice type utilization, average packet delay for each LTE/5G category, and others are used to assess game optimization for resource allocation

Elastic Demand Network Slicing based on Fuzzy Multicriteria

Elastic Demand Network Slicing based on Fuzzy Multicriteria

Intent-Oriented Network Slicing with Hypergraphs

Intent-Oriented Network Slicing with Hypergraphs

6G+ Networks Through Enhanced Efficiency and Sustainability With MADDPG-Driven Network Slicing in SoS Environments

6G+ Networks Through Enhanced Efficiency and Sustainability With MADDPG-Driven Network Slicing in SoS Environments

EDgE: Enhancing Predictability in Decentralized Network Slices Through Federated Learning—A Multi Layered Ecosystem

EDgE: Enhancing Predictability in Decentralized Network Slices Through Federated Learning—A Multi Layered Ecosystem

AI-Native End-to-End Network Slicing for Next-Generation Mission-Critical Services

AI-Native End-to-End Network Slicing for Next-Generation Mission-Critical Services

A Survey on Network Slicing Security: Attacks, Challenges, Solutions and Research Directions

A Survey on Network Slicing Security: Attacks, Challenges, Solutions and Research Directions

SAST-VNE: A Flexible Framework for Network Slicing in 6G Integrated Satellite-Terrestrial Networks

SAST-VNE: A Flexible Framework for Network Slicing in 6G Integrated Satellite-Terrestrial Networks

Controllable Queuing System with Elastic Traffic and Signals for Resource Capacity Planning in 5G Network Slicing

Fifth-generation (5G) networks provide network slicing capabilities, enabling the deployment of multiple logically isolated network slices on a single infrastructure platform to meet specific requirements of users. This paper focuses on modeling and analyzing resource capacity planning and reallocation for network slicing, specifically between two providers transmitting elastic traffic, such during as web browsing. A controller determines the need for resource reallocation and plans new resource capacity accordingly. A Markov decision process is employed in a controllable queuing system to find the optimal resource capacity for each provider. The reward function incorporates three network slicing principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization. To efficiently compute the optimal resource capacity planning policy, we developed an iterative algorithm that begins with maximum resource utilization as the starting point. Through numerical demonstrations, we show the optimal policy and metrics of resource reallocation for two services: web browsing and bulk data transfer. The results highlight fast convergence within three iterations and the effectiveness of the balanced three-principle approach in resource capacity planning for 5G network slicing.

Efficient Video QoE Prediction in Intelligent O-RAN

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into open-source elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user's subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program and control RAN functions using AI/ML-based models. We argue that various QoE parameters can be measured and operated by the RIC in O-RAN. We propose to improve the efficiency of O-RAN's radio resources by creating a RIC xApp that estimates the QoE measured using Video Mean Opinion of Score (MOS), and accurately optimizes the usage of radio resources across multiple network slices. We use predictive AI/ML-based models to accurately predict the QoE parameters in the network after which we can optimize the usage of network compo-nents leading to an enhanced user experience. Simulation results on 3 simulated data sets show that our proposed approach can achieve up to 95% QoE prediction accuracy.

Bandwidth abstraction and service rate instantiation for latency-bounded reliability provisioning in 5th generation wireless networks

The provisioning of the statistical reliability regard to latency is crux for the bursty traffic in the 5th generation (5G) wireless network slicing. Based on martingale theory, a novel analysis framework of the latency-bounded reliability is proposed. For the queuing system with bursty traffic, a Queuing Length Martingale is constructed to reveal the impact of the entanglement between the arrival process and the service process on the latency performance, where these two stochastic processes are sharply heterogeneous. The complex martingale parameters are determined. Relying on the time shift feature of the Queuing Length Martingale, a Latency Martingale is defined, which enables to analyze the latency-bounded unreliability in the martingale domain. Leveraging the stopping time theory, a tight upper bound of the unreliability regard to latency is obtained. A bandwidth abstraction and service rate instantiation scheme is designed for wireless network slicing. Bandwidth abstraction facilitates to decouple the statistical reliability requirements as the desired bandwidth of the wireless node. In the 5G access network, a transmission power allocation problem is formulated with the constraint of abstracted bandwidth. According to the access states and channel characteristics, the demanded transmission power is captured dynamically to instantiate the service rates. Simulation results highlight the effectiveness of the bandwidth abstraction and service rate instantiation scheme.