Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1137-1139· 0 citations· 4 references
Abstract
Sixth-generation (6G) wireless systems envision seamless coexistence between terrestrial networks (TNs) and non-terrestrial networks (NTNs), while the carbon footprint of dense radio access infrastructure has become a critical concern. This paper proposes carbon-aware traffic steering (CATS), a Near-RT RIC xApp for the open RAN (O-RAN) architecture. CATS introduces a network-wide virtual carbon queue into the global observation of a learning-based steering policy. The policy is implemented using a graph attention network (GAT) with type-aware attention to capture heterogeneous user equipment (UE) services and is trained via proximal policy optimization (PPO). Simulation results show that CATS significantly reduces net carbon emissions while preserving quality-of-service (QoS) satisfaction.
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
Future sixth-generation (6G)-oriented networks require programmable control that can adapt routing to latency and congestion without unsafe online exploration. This study evaluates offline multi-agent deep deterministic policy gradient (MADDPG) with behavior-adjusted training rewards for latency-aware path control in s...
A. Kyzyrkanov, Y. Nurakhov, Zhenis Otarbay et al.· Technologies· 0 citations
This paper proposes a deep reinforcement learning (DRL) meta-scheduler for twin-timescale resource allocation, which enforces isolation without inducing the broadband slice starvation characteristic of standard maximum-CQI heuristics, establishing a robust foundation for 6G O-RAN NTN resource allocation.
Víctor Vilchez, T. P. C. de Andrade, Edward Hinojosa et al.· 0 citations
To address load imbalance in low earth orbit (LEO) laser satellite networks (LSN), this paper proposes a deep reinforcement learning (DRL) based routing algorithm, which combines proximal policy optimization (PPO) and K-shortest path (KSP) strategies to transform the large-scale routing problem into a decision-making p...
Haoxin Li, Junling Yuan, Xu-Hong Li et al.· International Conference on...· 0 citations
The emergence of 5G and 6G advanced ecosystems demands highly adaptive resource management to orchestrate the specialised requirements of eMBB, URLLC, and mMTC network slices. In dense multi-cell environments, capturing complex spatial interdependencies and mitigating dynamic interference is paramount for maintaining Q...
Aya Kh. Ahmed, Nadia Al-Aboody, Hamed S. Al-Raweshidy· International Conference on...· 0 citations
Open Radio Access Networks (O-RAN) enable programmable radio access architectures in which learning-based intelligence at the RAN Intelligent Controller (RIC) supports adaptive resource management in future 6G systems. To address the dynamic and heterogeneous traffic conditions observed at the regional edge, we propose...
Pouya Mehdizadeh, Golshan Famitafreshi, John S. Vardakas et al.· International Symposium on C...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
