Loc X. Nguyen

Orcid: 0000-0001-5911-5847

According to our database1, Loc X. Nguyen authored at least 28 papers between 2022 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
DeepSeek-Inspired Exploration of RL-Based LLMs and Synergy with Wireless Networks: A Survey.
ACM Comput. Surv., May, 2026

Anchor-Aided Multi-User Semantic Communication with Adaptive Decoders.
CoRR, April, 2026

FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift.
CoRR, April, 2026

Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G.
CoRR, March, 2026

SemSpaceFL: A Collaborative Hierarchical Federated Learning Framework for Semantic Communication in 6G LEO Satellites.
IEEE Trans. Commun., 2026

A Contemporary Survey on Semantic Communications: Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding.
IEEE Commun. Surv. Tutorials, 2026

Hybrid Variational Quantum Circuit for Satellite Image Classification.
Proceedings of the 40th International Conference on Information Networking, 2026

2025
Semantic Communication Enabled 6G-NTN Framework: A Novel Denoising and Gateway Hop Integration Mechanism.
IEEE Trans. Wirel. Commun., December, 2025

Cross-Domain Federated Semantic Communication with Global Representation Alignment and Domain-Aware Aggregation.
CoRR, December, 2025

Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence.
CoRR, May, 2025

DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey.
CoRR, March, 2025

A Contemporary Survey on Semantic Communications:Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding.
CoRR, February, 2025

Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation.
IEEE Commun. Lett., January, 2025

2024
An Efficient Federated Learning Framework for Training Semantic Communication Systems.
IEEE Trans. Veh. Technol., October, 2024

Swin Transformer-Based Dynamic Semantic Communication for Multi-User With Different Computing Capacity.
IEEE Trans. Veh. Technol., June, 2024

Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization.
IEEE Wirel. Commun. Lett., May, 2024

Deep Reinforcement Learning-Based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications.
IEEE Internet Things J., April, 2024

Imbalance Cost-Aware Energy Scheduling for Prosumers Towards UAM Charging: A Matching and Multi-Agent DRL Approach.
IEEE Trans. Veh. Technol., March, 2024

Semantic Enabled 6G LEO Satellite Communication for Earth Observation: A Resource-Constrained Network Optimization.
Proceedings of the 2024 IEEE Global Communications Conference, 2024

2023
Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAV Networks: A Metaheuristic Approach.
IEEE Internet Things J., May, 2023

Training A Semantic Communication System with Federated Learning.
CoRR, 2023

Deep Reinforcement Learning based Spectral Efficiency Maximization in STAR-RIS-Assisted Indoor Outdoor Communication.
Proceedings of the NOMS 2023, 2023

Semantic Communication for AR-based Services in 5G and Beyond.
Proceedings of the International Conference on Information Networking, 2023

A New Chapter for Medical Image Generation: The Stable Diffusion Method.
Proceedings of the International Conference on Information Networking, 2023

Layer-wise Knowledge Distillation for Cross-Device Federated Learning.
Proceedings of the International Conference on Information Networking, 2023

Federated Multimodal Learning for IoT Applications: A Contrastive Learning Approach.
Proceedings of the 24st Asia-Pacific Network Operations and Management Symposium, 2023

2022
Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAVs Networks: A Meta-Heuristic Approach.
CoRR, 2022

An Encouraging Design for Data Owners to Join Multiple Co-existing Federated Learning.
Proceedings of the 23rd Asia-Pacific Network Operations and Management Symposium, 2022


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