Resource Allocation for Network Slices with Delay-Sensitive Multimedia Services

Gao, Jing; Zhou, Fanqin; Sun, Gang; Feng, Lei; Li, Wenjing; Qiu, Xuesong; Chen, Xiaolu

doi:10.1109/bmsb49480.2020.9379507

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result shown that the delay of end to end data transmission was 61.22 ms. The authors in [34] proposed a delay optimal resource allocation model between network slice and substrate network. And used uses the branch and bound method to solve it.…”

Section: B Cls For Network Managementmentioning

confidence: 99%

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Zhang,

Yang,

Dong

et al. 2024

IEEE Internet Things J.

View full text Add to dashboard Cite

Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of quality of experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure towards communication service providers (CSPs). In the 6th generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intentdriven closed-loop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in OpenAirInterface platform and realizing the CL control and management with near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

show abstract

Section: B Cls For Network Managementmentioning

confidence: 99%