9 citations · 13 across the 6 of their papers we have counts for
6 papers
Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks
Bo Liu, Haiyuan Li, Yuelin Liu +4
Mixture-of-Experts (MoE) architectures are increasingly deployed across 6G edge--cloud networks, where sparse activation reduces the computational footprint of each inference to on…
Deadline-Driven Hierarchical Agentic Resource Sharing for AI Services and RAN Functions in AI-RAN
Haiyuan Li, Yulei Wu, Dimitra Simeonidou
AI-RAN consolidates AI services and Radio Access Network (RAN) functions onto a unified, GPU-accelerated infrastructure at the network edge. However, compute sharing between real-t…
Multi-Agentic AI for Conflict-Aware rApp Policy Orchestration in Open RAN
Haiyuan Li, Yulei Wu, Dimitra Simeonidou
Open Radio Access Network (RAN) enables flexible, AI-driven control of mobile networks through disaggregated, multi-vendor components. In this architecture, xApps handle real-time…
Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks
Haiyuan Li, Hari Madhukumar, Shuangyi Yan +2
Generative AI (GenAI) has transformed applications in natural language processing and content creation, yet centralized inference remains hindered by high latency, limited customiz…
Lifecycle Management of Trustworthy AI Models in 6G Networks: The REASON Approach
Juan Parra-Ullauri, Xueqing Zhou, Shadi Moazzeni +11
Artificial Intelligence (AI) is expected to play a key role in 6G networks including optimising system management, operation, and evolution. This requires systematic lifecycle mana…
Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges
Haiyuan Li, Hari Madhukumar, Peizheng Li +6
Deep Reinforcement Learning (DRL) has emerged as a powerful solution for meeting the growing demands for connectivity, reliability, low latency and operational efficiency in advanc…