Hierarchical Multi Agent DRL for Soft Handovers Between Edge Clouds in Open RAN
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912270023393280 |
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| author | Giarrè, F. Meer, I. A. Masoudi, M. Ozger, M. Cavdar, C. |
| author_facet | Giarrè, F. Meer, I. A. Masoudi, M. Ozger, M. Cavdar, C. |
| contents | Multi-connectivity (MC) for aerial users via a set of ground access points offers the potential for highly reliable communication. Within an open radio access network (O-RAN) architecture, edge clouds (ECs) enable MC with low latency for users within their coverage area. However, ensuring seamless service continuity for transitional users-those moving between the coverage areas of neighboring ECs-poses challenges due to centralized processing demands. To address this, we formulate a problem facilitating soft handovers between ECs, ensuring seamless transitions while maintaining service continuity for all users. We propose a hierarchical multi-agent reinforcement learning (HMARL) algorithm to dynamically determine the optimal functional split configuration for transitional and non-transitional users. Simulation results show that the proposed approach outperforms the conventional functional split in terms of the percentage of users maintaining service continuity, with at most 4% optimality gap. Additionally, HMARL achieves better scalability compared to the static baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Multi Agent DRL for Soft Handovers Between Edge Clouds in Open RAN Giarrè, F. Meer, I. A. Masoudi, M. Ozger, M. Cavdar, C. Networking and Internet Architecture Multi-connectivity (MC) for aerial users via a set of ground access points offers the potential for highly reliable communication. Within an open radio access network (O-RAN) architecture, edge clouds (ECs) enable MC with low latency for users within their coverage area. However, ensuring seamless service continuity for transitional users-those moving between the coverage areas of neighboring ECs-poses challenges due to centralized processing demands. To address this, we formulate a problem facilitating soft handovers between ECs, ensuring seamless transitions while maintaining service continuity for all users. We propose a hierarchical multi-agent reinforcement learning (HMARL) algorithm to dynamically determine the optimal functional split configuration for transitional and non-transitional users. Simulation results show that the proposed approach outperforms the conventional functional split in terms of the percentage of users maintaining service continuity, with at most 4% optimality gap. Additionally, HMARL achieves better scalability compared to the static baselines. |
| title | Hierarchical Multi Agent DRL for Soft Handovers Between Edge Clouds in Open RAN |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.08493 |