37 citations · 77 across the 19 of their papers we have counts for
20 papers
Energy-Aware Compression-Computation Co-Adaptation for Latency Minimization in Multi-User Semantic Communication
Loc X. Nguyen, Yumin Park, Avi Deb Raha +4
Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positi…
A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles
S. M. Abtahiul Alam, Niloy Das, Apurba Adhikary +3
The development of smart transportation systems and the introduction of 6G wireless communication technologies have significantly changed vehicle network topologies. Future connect…
Semantic Communication for Non-Terrestrial Networks: A Survey from Platform Constraints to System Design
Loc X. Nguyen, Avi Deb Raha, Huy Q. Le +3
Sixth-generation networks are expected to extend connectivity beyond terrestrial infrastructure through non-terrestrial networks (NTNs) comprising satellites, high-altitude platfor…
Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics
Niloy Das, Apurba Adhikary, Sheikh Salman Hassan +4
Crime pattern analysis is critical for law enforcement and predictive policing, yet the surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced da…
Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery
Luyao Zou, Fei Pan, Jueying Li +4
Federated learning (FL) has recently become a promising solution for analyzing remote sensing satellite imagery (RSSI). However, the large scale and inherent data heterogeneity of…
SemSpaceFL: A Collaborative Hierarchical Federated Learning Framework for Semantic Communication in 6G LEO Satellites
Loc X. Nguyen, Sheikh Salman Hassan, Yu Min Park +3
The advent of the sixth-generation (6G) wireless networks, enhanced by artificial intelligence, promises ubiquitous connectivity through Low Earth Orbit (LEO) satellites. These sat…
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining
Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4
In this paper, we propose the FedFeat+ framework, which distinctively separates feature extraction from classification. We develop a two-tiered model training process: following lo…
Security Risks in Vision-Based Beam Prediction: From Spatial Proxy Attacks to Feature Refinement
Avi Deb Raha, Kitae Kim, Mrityunjoy Gain +4
The rapid evolution towards the sixth-generation (6G) networks demands advanced beamforming techniques to address challenges in dynamic, high-mobility scenarios, such as vehicular…
A Deep Incremental Framework for Multi-Service Multi-Modal Devices in NextG AI-RAN Systems
Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4
In this paper, we propose a deep incremental framework for efficient RAN management, introducing the Multi-Service-Modal UE (MSMU) system, which enables a single UE to handle eMBB…
Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence
Yu Qiao, Apurba Adhikary, Huy Q. Le +3
Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL models remain susceptible to ad…
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
Yu Qiao, Apurba Adhikary, Kitae Kim +3
Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data…
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach
Luyao Zou, Yu Min Park, Chu Myaet Thwal +3
Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an…
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning
Sheikh Salman Hassan, Yu Min Park, Yan Kyaw Tun +3
In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote use…
Semantic Communication Enabled 6G-NTN Framework: A Novel Denoising and Gateway Hop Integration Mechanism
Loc X. Nguyen, Sheikh Salman Hassan, Yan Kyaw Tun +3
The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, r…
Active STAR-RIS Empowered Edge System for Enhanced Energy Efficiency and Task Management
Pyae Sone Aung, Kitae Kim, Yan Kyaw Tun +2
The proliferation of data-intensive and low-latency applications has driven the development of multi-access edge computing (MEC) as a viable solution to meet the increasing demands…
Semantic Enabled 6G LEO Satellite Communication for Earth Observation: A Resource-Constrained Network Optimization
Sheikh Salman Hassan, Loc X. Nguyen, Yan Kyaw Tun +2
Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major chall…
Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation
Loc X. Nguyen, Kitae Kim, Ye Lin Tun +4
Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, a…
Data Service Maximization in Space-Air-Ground Integrated 6G Networks
Nway Nway Ei, Kitae Kim, Yan Kyaw Tun +2
Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and qual…
Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization
Pyae Sone Aung, Loc X. Nguyen, Yan Kyaw Tun +2
Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establis…
An Efficient Federated Learning Framework for Training Semantic Communication System
Loc X. Nguyen, Huy Q. Le, Ye Lin Tun +4
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication…