most citedNetMind: Adaptive RAN Baseband Function Placement by GCN Encoding and Maze-solving DRL

7 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2026

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…

eess.SY2024

Fast Link Recovery via PTP-synchronized Nanosecond Optical Switching

V. Yokar, A. Mehrpooya, Y. Teng +5

This paper proposes and validates a PTP-synchronized 8.4ns optical switching with a 100ns jitter at the switching edges. This approach is adopted and demonstrated for instant netwo…

cs.NI2024★ 4 cited

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…

cs.NI2024★ 1 cited

DRL-Assisted Dynamic QoT-Aware Service Provisioning in Multi-Band Elastic Optical Networks

Yiran Teng, Carlos Natalino, Farhad Arpanaei +4

We propose a DRL-assisted approach for service provisioning in multi-band elastic optical networks. Our simulation environment uses an accurate QoT estimator based on the GN/EGN mo…

cs.NI2024★ 7 cited

NetMind: Adaptive RAN Baseband Function Placement by GCN Encoding and Maze-solving DRL

Haiyuan Li, Peizheng Li, Karcius Day Assis +5

The disaggregated and hierarchical architecture of advanced RAN presents significant challenges in efficiently placing baseband functions and user plane functions in conjunction wi…