2021
DOI: 10.48550/arxiv.2107.05991
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Learning based E2E Energy Efficient in Joint Radio and NFV Resource Allocation for 5G and Beyond Networks

Abstract: In this paper, we propose a joint radio and core resource allocation framework for NFV-enabled networks. In the proposed system model, the goal is to maximize energy efficiency (EE), by guaranteeing end-to-end (E2E) quality of service (QoS) for different service types. To this end, we formulate an optimization problem in which power and spectrum resources are allocated in the radio part. In the core part, the chaining, placement, and scheduling of functions are performed to ensure the QoS of all users. This jo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 50 publications
0
1
0
Order By: Relevance
“…Learning-based energy-efficient proposed in [10] for resource allocation in the radio access network and NFV in the 5G networks and beyond. The proposed algorithm can jointly optimize the radio and NFV resources to minimize the network's energy consumption while meeting the users' QoS requirements.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Learning-based energy-efficient proposed in [10] for resource allocation in the radio access network and NFV in the 5G networks and beyond. The proposed algorithm can jointly optimize the radio and NFV resources to minimize the network's energy consumption while meeting the users' QoS requirements.…”
Section: Literature Reviewmentioning
confidence: 99%