8 papers
Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey
Jie Lu, Peihao Yan, Qijun Wang +2
The evolution toward sixth-generation (6G) networks is transforming the radio access network (RAN) into a programmable and intelligent control platform that must continuously adapt…
Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation
Jie Lu, Peihao Yan, Pang-Ning Tan +2
Open Radio Access Networks (O-RAN) are increasingly adopting data-driven control through Deep Reinforcement Learning (DRL) to optimize complex tasks such as network slicing and mob…
TARMM: Scaling Delay-Critical Edge AI Offloading in 5G O-RAN via Temporal Graph Mobility Management
Peihao Yan, Yun Chen, Jie Lu +2
Emerging delay-critical edge AI applications, such as VR perception and real-time video analytics, impose stringent latency and reliability requirements on 5G networks. However, ex…
RadEar: A Self-Supervised RF Backscatter System for Voice Eavesdropping and Separation
Qijun Wang, Peihao Yan, Chunqi Qian +1
Eavesdropping on voice conversations presents a growing threat to personal privacy and information security. In this paper, we present RadEar, a novel RF backscatter-based system d…
Integrating Health Sensing into Cellular Networks: Human Sleep Monitoring Using 5G Signals
Ruxin Lin, Peihao Yan, Jie Lu +2
Cellular networks offer a unique opportunity to enable device-free and wide-area health monitoring by exploiting the sensitivity of radio-frequency (RF) propagation to human physio…
EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN
Jie Lu, Peihao Yan, Huacheng Zeng
With over 3.5 million 5G base stations deployed globally, their collective energy consumption (projected to exceed 131 TWh annually) raises significant concerns over both operation…