4 citations · 5 across the 4 of their papers we have counts for
4 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…
Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
Yuelin Liu, Haiyuan Li, Xenofon Vasilakos +2
Future networks (including 6G) are poised to accelerate the realisation of Internet of Everything. However, it will result in a high demand for computing resources to support new s…
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…
Incremental DRL-Based Resource Management for Dynamic Network Slicing in an Urban-Wide Testbed
Haiyuan Li, Yuelin Liu, Hari Madhukumar +6
Multi-access edge computing provides localized resources within mobile networks to address the requirements of emerging latency-sensitive and computing-intensive applications. At t…