38 citations · 62 across the 17 of their papers we have counts for
18 papers
Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement
Loc X. Nguyen, Avi Deb Raha, Huy Q. Le +3
Humanoid robots are becoming an important part of embodied artificial intelligence, driven by advances in reinforcement learning for locomotion, world models for prediction, and vi…
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…
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…
Anchor-Aided Multi-User Semantic Communication with Adaptive Decoders
Loc X. Nguyen, Phuong-Nam Tran, Trung Thanh Pham +4
Semantic communication (SemCom) is accelerating its momentum to catch up with the massive increase in users' demands in both quantity and quality, with the assistance of advanced d…
Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G
Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +6
The shift toward user-customized on-device learning places new demands on wireless systems: models must be trained on diverse, distributed data while meeting strict latency, bandwi…
Cross-Domain Federated Semantic Communication with Global Representation Alignment and Domain-Aware Aggregation
Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +7
Semantic communication can significantly improve bandwidth utilization in wireless systems by exploiting the meaning behind raw data. However, the advancements achieved through sem…
DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +3
Deep Joint Source-Channel Coding (Deep-JSCC) has emerged as a promising semantic communication approach for wireless image transmission by jointly optimizing source and channel cod…
A Survey on Foundation Models for Personalized Federated Intelligence
Yu Qiao, Huy Q. Le, Avi Deb Raha +7
The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remark…
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…
A Contemporary Survey on Semantic Communications:Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding
Loc X. Nguyen, Avi Deb Raha, Pyae Sone Aung +3
Semantic communication is emerging as the next pillar in wireless communication technology due to its transformative capabilities in reducing communication overhead, enhancing robu…
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +2
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Gener…
Advancing Ultra-Reliable 6G: Transformer and Semantic Localization Empowered Robust Beamforming in Millimeter-Wave Communications
Avi Deb Raha, Kitae Kim, Apurba Adhikary +3
Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment…
CCC: Color Classified Colorization
Mrityunjoy Gain, Avi Deb Raha, Rameswar Debnath
Automatic colorization of gray images with objects of different colors and sizes is challenging due to inter- and intra-object color variation and the small area of the main object…
CCC++: Optimized Color Classified Colorization with Segment Anything Model (SAM) Empowered Object Selective Color Harmonization
Mrityunjoy Gain, Avi Deb Raha, Rameswar Debnath
In this paper, we formulate the colorization problem into a multinomial classification problem and then apply a weighted function to classes. We propose a set of formulas to transf…
Generative AI-driven Semantic Communication Framework for NextG Wireless Network
Avi Deb Raha, Md. Shirajum Munir, Apurba Adhikary +2
This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts th…
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
Yu Qiao, Md. Shirajum Munir, Apurba Adhikary +4
Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution…