Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915920929095680 |
|---|---|
| author | Giwa, Oluwaseyi Shock, Jonathan Toit, Jaco Du Awodumila, Tobi |
| author_facet | Giwa, Oluwaseyi Shock, Jonathan Toit, Jaco Du Awodumila, Tobi |
| contents | Dynamic resource allocation in open radio access network (O-RAN) heterogeneous networks (HetNets) presents a complex optimisation challenge under varying user loads. We propose a near-real-time RAN intelligent controller (Near-RT RIC) xApp utilising deep reinforcement learning (DRL) to jointly optimise transmit power, bandwidth slicing, and user scheduling. Leveraging real-world network topologies, we benchmark proximal policy optimisation (PPO) and twin delayed deep deterministic policy gradient (TD3) against standard heuristics. Our results demonstrate that the PPO-based xApp achieves a superior trade-off, reducing network energy consumption by up to 70% in dense scenarios and improving user fairness by more than 30% compared to throughput-greedy baselines. These findings validate the feasibility of centralised, energy-aware AI orchestration in future 6G architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning Giwa, Oluwaseyi Shock, Jonathan Toit, Jaco Du Awodumila, Tobi Machine Learning Networking and Internet Architecture Signal Processing Dynamic resource allocation in open radio access network (O-RAN) heterogeneous networks (HetNets) presents a complex optimisation challenge under varying user loads. We propose a near-real-time RAN intelligent controller (Near-RT RIC) xApp utilising deep reinforcement learning (DRL) to jointly optimise transmit power, bandwidth slicing, and user scheduling. Leveraging real-world network topologies, we benchmark proximal policy optimisation (PPO) and twin delayed deep deterministic policy gradient (TD3) against standard heuristics. Our results demonstrate that the PPO-based xApp achieves a superior trade-off, reducing network energy consumption by up to 70% in dense scenarios and improving user fairness by more than 30% compared to throughput-greedy baselines. These findings validate the feasibility of centralised, energy-aware AI orchestration in future 6G architectures. |
| title | Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning |
| topic | Machine Learning Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2509.25284 |