Related papers: OnSlicing: Online End-to-End Network Slicing with …
Network slicing is an effective 5G concept for improved resource utilization and service scalability tailored to users (UEs) requirements. According to the standardization, 5G system should support UEs through specification of its…
In this paper, we propose a solution to proactively mitigate Distributed Denial-of-Service attacks in 5G core network slicing using slice isolation. Network slicing is one of the key technologies that allow 5G networks to offer dedicated…
Through network slicing, different requirements of different applications and services can be met. These requirements can be in terms of latency, bandwidth, mobility support, defining service area, as well as security. Through fine and…
Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a huge amount of memory and computational operations. However,…
The technology of network slicing, as the most characteristic feature of the fifth generation (5G) wireless networks, manages the resources and network functions in heterogeneous and logically isolated slices on the top of a shared physical…
We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process. We propose a framework based on Deep Reinforcement Learning (DRL) combined with a…
In the context of Fifth Generation (5G) mobile networks, the concept of "Slice as a Service" (SlaaS) promotes mobile network operators to flexibly share infrastructures with mobile service providers and stakeholders. However, it also…
In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total…
Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency…
Next generation wireless networks are expected to support diverse vertical industries and offer countless emerging use cases. To satisfy stringent requirements of diversified services, network slicing is developed, which enables…
Machine learning applied to architecture design presents a promising opportunity with broad applications. Recent deep reinforcement learning (DRL) techniques, in particular, enable efficient exploration in vast design spaces where…
The diverse requirements of beyond 5G services increase design complexity and demand dynamic adjustments to the network parameters. This can be achieved with slicing and programmable network architectures such as the open radio access…
Network slicing over space division multiplexed elastic optical networks (SDM EONs) enables efficient multiservice provisioning on a shared optical substrate. However, embedding such slices requires coordinated spectrum and compute resource…
Virtualization in 5G and beyond networks allows the creation of virtual networks, or network slices, tailored to meet the requirements of various applications. However, this flexibility introduces several challenges for infrastructure…
6G networks are composed of subnetworks expected to meet ultra-reliable low-latency communication (URLLC) requirements for mission-critical applications such as industrial control and automation. An often-ignored aspect in URLLC is…
RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive…
In the current era, the next-generation networks like 5th generation (5G) and 6th generation (6G) networks require high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network…
Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal{Q}$-learning, DQL) is assumed to be an appropriate algorithm to solve the…
Network slicing enables the deployment of multiple dedicated virtual sub-networks, i.e. slices on a shared physical infrastructure. Unlike traditional one-size-fits-all resource provisioning schemes, each network slice (NS) in 5G is…
Offline reinforcement learning (RL) provides a promising direction to exploit massive amount of offline data for complex decision-making tasks. Due to the distribution shift issue, current offline RL algorithms are generally designed to be…