Related papers: Reinforcement Learning Based Resource Allocation f…
Network slicing is a promising technology that allows mobile network operators to efficiently serve various emerging use cases in 5G. It is challenging to optimize the utilization of network infrastructures while guaranteeing the…
Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular customer or application. The…
Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems towards the smart grid. However, the inherent heterogeneity in smart grid services and requirements pose significant challenges…
Network slicing is one of the key features for 5G networks to be able to accommodate the anticipated diversity of applications and business actors in a resource-efficient manner. While significant progress has already been achieved at 3GPP…
Network slicing allows Telecom Operators (TOs) to support service provisioning with diverse Service Level Agreements (SLAs). The combination of network slicing and Open Radio Access Network (RAN) enables TOs to provide more customized…
Implications of the advancements in the area of artificial intelligence to the wireless communications is extremely significant, especially in terms of resource management. In this paper, a Retrieval-Augmented Generation (RAG)-empowered…
5G radio access network (RAN) slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile…
The fifth generation and beyond wireless communication will support vastly heterogeneous services and use demands such as massive connection, low latency and high transmission rate. Network slicing has been envisaged as an efficient…
Network slicing is a key enabler for 5G to support various applications. Slices requested by service providers (SPs) have heterogeneous quality of service (QoS) requirements, such as latency, throughput, and jitter. It is imperative that…
Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing…
Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design…
Online Network Resource Allocation (ONRA) for service provisioning is a fundamental problem in communication networks. As a sequential decision-making under uncertainty problem, it is promising to approach ONRA via Reinforcement Learning…
5G and edge computing will serve various emerging use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology for creating virtual networks that can…
Network slicing manages network resources as virtual resource blocks (RBs) for the 5G Radio Access Network (RAN). Each communication request comes with quality of experience (QoE) requirements such as throughput and latency/deadline, which…
In this paper, we tackle the challenge of radio access network (RAN) slicing within an open RAN (O-RAN) architecture. Our focus centers on a network that includes multiple mobile virtual network operators (MVNOs) competing for physical…
Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to…
Wireless network optimization has been becoming very challenging as the problem size and complexity increase tremendously, due to close couplings among network entities with heterogeneous service and resource requirements. By continuously…
Modern wireless networks must adapt to dynamic conditions while efficiently managing diverse service demands. Traditional deep reinforcement learning (DRL) struggles in these environments, as scattered and evolving feedback makes optimal…
Algorithmic innovation can unleash the potential of the beyond 5G (B5G)/6G communication systems. Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full…
Network slicing-based communication systems can dynamically and efficiently allocate resources for diversified services. However, due to the limitation of the network interface on channel access and the complexity of the resource…