Towards Integrated Energy-Communication-Transportation Hub: A Base-Station-Centric Design in 5G and Beyond
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911116240617472 |
|---|---|
| author | Shen, Linfeng Wu, Guanzhen Zhang, Cong Fan, Xiaoyi Liu, Jiangchuan |
| author_facet | Shen, Linfeng Wu, Guanzhen Zhang, Cong Fan, Xiaoyi Liu, Jiangchuan |
| contents | The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and participating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15833 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Integrated Energy-Communication-Transportation Hub: A Base-Station-Centric Design in 5G and Beyond Shen, Linfeng Wu, Guanzhen Zhang, Cong Fan, Xiaoyi Liu, Jiangchuan Networking and Internet Architecture Distributed, Parallel, and Cluster Computing The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and participating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging. |
| title | Towards Integrated Energy-Communication-Transportation Hub: A Base-Station-Centric Design in 5G and Beyond |
| topic | Networking and Internet Architecture Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.15833 |