MARSRA: Mobility-Aware RAN Slicing Resource Allocation for Open RAN Deployments

Sina Ebrahimi; Faouzi Bouali; Olivier C. L. Haas

MARSRA: Mobility-Aware RAN Slicing Resource Allocation for Open RAN Deployments

Sina Ebrahimi, Faouzi Bouali, Olivier C. L. Haas

Centre for Future Transport and Cities

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

2 Downloads (Pure)

Abstract

The delivery of diverse services in 5G/6G networks is made possible through Network Slicing (NS). This paper focuses on Radio Access Network (RAN) slicing in the presence of user mobility, which can greatly affect the performance of the slices provided by mobile network operators. We provide a novel application called Mobility-Aware RAN Slicing Resource Allocation (MARSRA) xApp by utilizing the programmability of the emerging Open RAN architecture. The MARSRA xApp strives to maximize user satisfaction by leveraging Channel State Information (CSI) to meet user requirements. We introduce a joint online problem that considers user assignment, Physical Resource Block (PRB) allocation, and power allocation in Open RAN deployments. To cope with the nonlinearity and complexity of the formulated problem, a Deep Reinforcement Learning (DRL) methodology is devised. It relies on a Soft Actor-Critic (SAC) algorithm that incorporates reduced state and action spaces to address the uncertainty caused by mobility. The simulation results demonstrate that the proposed SAC method is highly effective, with an improvement of at least 22% in overall user satisfaction compared to two traditional baseline schemes.

Original language	English
Title of host publication	Proceedings of the IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Publisher	IEEE
Pages	(In-Press)
Number of pages	6
Volume	(In-Press)
Publication status	Accepted/In press - 29 Aug 2024
Event	IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) - Athens, Greece, Athens, Greece Duration: 21 Oct 2024 → 23 Oct 2024 https://camad2024.ieee-camad.org/

Conference

Conference	IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Abbreviated title	CAMAD
Country/Territory	Greece
City	Athens
Period	21/10/24 → 23/10/24
Internet address	https://camad2024.ieee-camad.org/

Bibliographical note

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Keywords

Open RAN
Network Slicing
Dynamic Resource Allocation
Mobility
xApps
Soft Actor-Critic (SAC)
DRL

Embargoed Document

Ebrahimi2024AAM
Accepted author manuscript, 1.89 MB
Embargo ends: 11/02/25

1 Article

Resource Management From Single-domain 5G to End-to-End 6G Network Slicing: A Survey
Ebrahimi, S., Bouali, F. & Haas, O. C. L., 17 Apr 2024, (E-pub ahead of print) In: IEEE Communications Surveys and Tutorials. (In-Press), p. (In-Press) 31 p.
Research output: Contribution to journal › Article › peer-review

Open Access
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6 Citations (Scopus)

32942 Downloads (Pure)

Cite this

MARSRA: Mobility-Aware RAN Slicing Resource Allocation for Open RAN Deployments. / Ebrahimi, Sina; Bouali, Faouzi; Haas, Olivier C. L.
Proceedings of the IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Vol. (In-Press) IEEE, 2024. p. (In-Press).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Ebrahimi, S, Bouali, F & Haas, OCL 2024, MARSRA: Mobility-Aware RAN Slicing Resource Allocation for Open RAN Deployments. in Proceedings of the IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). vol. (In-Press), IEEE, pp. (In-Press), IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Athens, Greece, 21/10/24.

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N2 - The delivery of diverse services in 5G/6G networks is made possible through Network Slicing (NS). This paper focuses on Radio Access Network (RAN) slicing in the presence of user mobility, which can greatly affect the performance of the slices provided by mobile network operators. We provide a novel application called Mobility-Aware RAN Slicing Resource Allocation (MARSRA) xApp by utilizing the programmability of the emerging Open RAN architecture. The MARSRA xApp strives to maximize user satisfaction by leveraging Channel State Information (CSI) to meet user requirements. We introduce a joint online problem that considers user assignment, Physical Resource Block (PRB) allocation, and power allocation in Open RAN deployments. To cope with the nonlinearity and complexity of the formulated problem, a Deep Reinforcement Learning (DRL) methodology is devised. It relies on a Soft Actor-Critic (SAC) algorithm that incorporates reduced state and action spaces to address the uncertainty caused by mobility. The simulation results demonstrate that the proposed SAC method is highly effective, with an improvement of at least 22% in overall user satisfaction compared to two traditional baseline schemes.

AB - The delivery of diverse services in 5G/6G networks is made possible through Network Slicing (NS). This paper focuses on Radio Access Network (RAN) slicing in the presence of user mobility, which can greatly affect the performance of the slices provided by mobile network operators. We provide a novel application called Mobility-Aware RAN Slicing Resource Allocation (MARSRA) xApp by utilizing the programmability of the emerging Open RAN architecture. The MARSRA xApp strives to maximize user satisfaction by leveraging Channel State Information (CSI) to meet user requirements. We introduce a joint online problem that considers user assignment, Physical Resource Block (PRB) allocation, and power allocation in Open RAN deployments. To cope with the nonlinearity and complexity of the formulated problem, a Deep Reinforcement Learning (DRL) methodology is devised. It relies on a Soft Actor-Critic (SAC) algorithm that incorporates reduced state and action spaces to address the uncertainty caused by mobility. The simulation results demonstrate that the proposed SAC method is highly effective, with an improvement of at least 22% in overall user satisfaction compared to two traditional baseline schemes.

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KW - DRL

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ER -

MARSRA: Mobility-Aware RAN Slicing Resource Allocation for Open RAN Deployments

Abstract

Conference

Bibliographical note

Keywords

Embargoed Document

Fingerprint

Research output

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

Cite this