6 papers
Artificial Intelligence for Spatially Reconfigurable Antennas: Movable, Fluid, and Pinching Antenna Systems
Nguyen Cong Luong, Zeping Sui, Thai-Hoc Vu +10
Recently, sixth-generation (6G) wireless networks have moved beyond fixed-array designs toward antenna architectures that can adapt their spatial configuration to specific environm…
From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks
Nguyen Cong Luong, Zeping Sui, Jie Cao +9
Deep reinforcement learning (DRL) has long been a promising solution for sequential resource management in wireless networks. However, conventional DRL methods are fundamentally li…
Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks
Nguyen Cong Luong, Shaohan Feng, Nguyen Duc Hai +10
Reinforcement Learning (RL) has long been a powerful solution to various problems in communication networks. However, traditional RL models still face with several limitations. Not…
Dynamic Antenna Placement for Mobile Users in Urban Micro Pinching-Antenna Systems
Qiushi Zhao, Zihan Feng, Ximing Xie +3
The pinching-antenna systems (PASS) enable blockage mitigation in urban micro (UMi) networks through flexible antenna placement. However, the joint optimization of antenna position…
Physics-Informed Deep Recurrent Back-Projection Network for Tunnel Propagation Modeling
Kunyu Wu, Qiushi Zhao, Jingyi Zhou +4
Accurate and efficient modeling of radio wave propagation in railway tunnels is is critical for ensuring reliable communication-based train control (CBTC) systems. Fine-grid parabo…
Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning
Kunyu Wu, Qiushi Zhao, Zihan Feng +4
Urban railway systems increasingly rely on communication based train control (CBTC) systems, where optimal deployment of access points (APs) in tunnels is critical for robust wirel…