Sep 2026· IEEE International Symposium on Personal, Indoor and Mobile Radio Communications· pp. 1-7· 0 citations· 15 references
Abstract
Open radio access network (Open RAN) disaggregation across the distributed unit (DU), centralized unit (CU), and near-real-time radio intelligent controller (near-RT RIC) enables programmable, hierarchical radio access control. This architectural split allows independent optimization of short-term resource management at the DU and long-term mobility decisions at the near-RT RIC, making it ideal for beyond fifth-generation (B5G) networks leveraging unmanned aerial vehicle (UAV)-mounted aerial radio units (ARUs). However, these ARUs must contend with heterogeneous user equipment (UE) velocities and varying service demands. To ensure seamless connectivity, this paper proposes a handover-aware mobility management framework incorporating an adaptive time-to-trigger (TTT) mechanism. The framework is formulated as a mixed-integer nonlinear programming (MINLP) problem using a weighted-sum scalarization approach to maximize aggregate data rate while minimizing handovers, subject to qualityof-service (QoS), power control, and fronthaul capacity constraints. To mitigate computational complexity, a two-timescale heuristic solution executes short-term resource and power allocation at the DU, and long-term user association and handover management at the near-RT RIC. Simulation results demonstrate that the proposed approach effectively controls handover frequency and improves throughput, reducing handovers by 60.37% and achieving up to a $3.3 \times$ data rate improvement over the analytical-TTT baseline.
This paper proposes a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices and UAVs act as heterogeneous agents and utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers.
Ming Cheng, Canlin Zhu, Jian-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
This paper places the terrestrial-satellite UAV handover decision inside the near-real-time RIC as an xApp, using a deep reinforcement learning (DRL) framework that combines a Deep Q-Network (DQN) with Prioritized Experience Replay (PER) and a reward shaped to weigh the long-term cost of switching against the reliabili...
Anirudh Warrier, Saba Al-Rubaye· IEEE Access· 0 citations
The integrated geostationary Earth orbit (GEO)-multibeam and low Earth orbit (LEO)-uncrewed aerial vehicle (UAV) network has emerged as a promising paradigm to enhance the coverage and capacity of terrestrial networks. However, due to the high dynamics of the network and the heterogeneous service demands of user equipm...
Feng Yang, Meng Li, Peng-Bo Si et al.· IEEE Transactions on Wireles...· 0 citations
This paper addresses the minimization problem of long-term end-to-end delay within a heterogeneous three-tier unmanned aerial vehicle (UAV)-assisted mobile edge computing network featuring two-hop forwarding. The joint optimization of sensor-node-aggregator association, access/backhaul bandwidth allocation, UAV traject...
Rui-Yi Yao, Wei-Yi Hu, Le-Heng Qi et al.· IEEE International Symposium...· 0 citations
A Non-Real-Time RIC rApp is realized as a permutation-equivariant DeepSets Soft Actor-Critic scheduler that treats the users as an unordered set, trained in a Sionna RT ray traced channel, and a Near-Real-Time RIC xApp allocates per-user resources within that budget.
In the context of 6G ubiquitous connectivity, the space-air-ground-sea integrated network (SAGSIN) emerges as a new paradigm for pervasive service provisioning. To support expanding maritime activities in infrastructure-scarce ocean areas, we propose an innovative dynamic task and resource scheduling approach for SAGSI...
Yu-Fei Ye, Shi-Jian Gao, Xin-Hu Zheng et al.· 0 citations
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