9 papers
UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach
Dao Lan Vy Dinh, Anh Nguyen Thi Mai, Hung Tran +6
This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple gr…
Resilience Optimization in 6G and Beyond Integrated Satellite-Terrestrial Networks: A Deep Reinforcement Learning Approach
Dinh-Hieu Tran, Nguyen Van Huynh, Van Nhan Vo +3
Ensuring network resilience in 6G and beyond is essential to maintain service continuity during base station (BS) outages due to failures, disasters, attacks, or energy-saving oper…
Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery
Ti Ti Nguyen, Thanh-Dung Le, Vu Nguyen Ha +6
Modern Earth Observation (EO) systems increasingly rely on high-resolution imagery to support critical applications such as environmental monitoring, disaster response, and land-us…
Collaborative Intelligence for UAV-Satellite Network Slicing: Towards a Joint QoS-Energy-Fairness MADRL Optimization
Thanh-Dao Nguyen, Ngoc-Tan Nguyen, Thai-Duong Nguyen +3
Non terrestrial networks are critical for achieving global 6G coverage, yet efficient resource management in aerial and space environments remains challenging due to limited onboar…
Quantum Reinforcement Learning for 6G and Beyond Wireless Networks
Dinh-Hieu Tran, Thai Duong Nguyen, Thanh-Dao Nguyen +10
While 5G is being deployed worldwide, 6G is receiving increasing attention from researchers to meet the growing demand for higher data rates, lower latency, higher density, and sea…
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning
Nguyen Van Huynh, Bolun Zhang, Dinh-Hieu Tran +5
Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming…