3 citations · 9 across the 13 of their papers we have counts for
13 papers
TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding
Achintha Wijesinghe, Weiwei Wang, Suchinthaka Wanninayaka +2
Recent advancements in generative artificial intelligence have introduced groundbreaking approaches to innovating next-generation semantic communication, which prioritizes conveyin…
Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration
Fupei Guo, Achintha Wijesinghe, Songyang Zhang +1
Semantic communications represent a new paradigm of next-generation networking that shifts bit-wise data delivery to conveying the semantic meanings for bandwidth efficiency. To ef…
Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications
Yu-Chieh Chao, Yubei Chen, Weiwei Wang +4
Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry…
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications
Yueling Zhou, Achintha Wijesinghe, Yue Wang +2
Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To e…
LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency
Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang +3
The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions. The co…
Diff-GO: Enhancing Diffusion Models for Goal-Oriented Communications
Suchinthaka Wanninayaka, Achintha Wijesinghe, Weiwei Wang +3
The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communicati…
TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation
Yueling Zhou, Achintha Wijesinghe, Yibo Ma +2
To characterize radio frequency (RF) signal power distribution in wireless communication systems, the radiomap is a useful tool for resource allocation and network management. Usua…
Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency
Achintha Wijesinghe, Songyang Zhang, Suchinthaka Wanninayaka +2
The latest advances in artificial intelligence (AI) present many unprecedented opportunities to achieve much improved bandwidth saving in communications. Unlike conventional commun…
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
Siyu Qi, Achintha Wijesinghe, Lahiru D. Chamain +1
Among applications of deep learning (DL) involving low cost sensors, remote image classification involves a physical channel that separates edge sensors and cloud classifiers. Trad…
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning
Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Recent advances of generative learning models are accompanied by the growing interest in federated learning (FL) based on generative adversarial network (GAN) models. In the contex…
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data
Achintha Wijesinghe, Songyang Zhang, Siyu Qi +1
To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL)…
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preser…
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
Songyang Zhang, Achintha Wijesinghe, Zhi Ding
Outdoor radio map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. Radio map describes spatial…