Projects per year
Abstract
The advent of 5G networks necessitates more flexible
and intelligent architectures, prompting a shift from conventional
models to Open Radio Access Networks (O-RAN) augmented
with integrated network slicing (NS). The combination of ORAN with a cell-free architecture improves both coverage and
performance, while NS facilitates dynamic resource allocation to
support diverse services, such as ultra-reliable low-latency communications (uRLLC) and enhanced mobile broadband (eMBB).
This paper introduces a novel NS-enabled, cell-free O-RAN
framework designed to optimize resource allocation and power
control. In contrast to traditional methods, we adopt a Deep
Reinforcement Learning (DRL) approach, leveraging the Soft
Actor-Critic (SAC) algorithm to dynamically allocate power and
resources across distributed access points (APs), while simultaneously ensuring the efficient management of network slices. The
proposed framework aims to maximize the number of admitted
slices while minimizing network costs, ensuring optimal data
rates for eMBB and low latency for uRLLC services. Through
this approach, we demonstrate enhanced flexibility, scalability,
and performance in dynamic 5G wireless environments.
and intelligent architectures, prompting a shift from conventional
models to Open Radio Access Networks (O-RAN) augmented
with integrated network slicing (NS). The combination of ORAN with a cell-free architecture improves both coverage and
performance, while NS facilitates dynamic resource allocation to
support diverse services, such as ultra-reliable low-latency communications (uRLLC) and enhanced mobile broadband (eMBB).
This paper introduces a novel NS-enabled, cell-free O-RAN
framework designed to optimize resource allocation and power
control. In contrast to traditional methods, we adopt a Deep
Reinforcement Learning (DRL) approach, leveraging the Soft
Actor-Critic (SAC) algorithm to dynamically allocate power and
resources across distributed access points (APs), while simultaneously ensuring the efficient management of network slices. The
proposed framework aims to maximize the number of admitted
slices while minimizing network costs, ensuring optimal data
rates for eMBB and low latency for uRLLC services. Through
this approach, we demonstrate enhanced flexibility, scalability,
and performance in dynamic 5G wireless environments.
| Original language | English |
|---|---|
| Title of host publication | 2025 IEEE Wireless Communications and Networking Conference (WCNC) |
| Publisher | IEEE |
| Number of pages | 6 |
| Publication status | Published - 27 Mar 2025 |
Bibliographical note
© IEEE 2025. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.Projects
- 1 Finished
-
YO-RAN
Burr, A. G. (Principal investigator), Ahmadi, H. (Co-investigator) & Grace, D. (Co-investigator)
21/02/23 → 31/03/25
Project: Research project (funded) › Research