Related papers: Digital-SC: Digital Semantic Communication with Ad…
Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner. They do…
Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions. Utilization of deep neural networks…
Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods neglect that not all semantic representations across domains…
Most existing semantic communication systems employ analog modulation, which is incompatible with modern digital communication systems. Although several digital transmission approaches have been proposed to address this issue, an end-to-end…
Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to address the time-varying channels in wireless communication…
Semantic communications have been envisioned as a potential technique that goes beyond Shannon paradigm. Unlike modern communications that provide bit-level security, the eaves-dropping of semantic communications poses a significant risk of…
As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification…
Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works…
Deep learning based semantic communication (DeepSC) system has emerged as a promising paradigm for efficient wireless transmission. However, existing image DeepSC methods, frequently encounter challenges in balancing rate-distortion…
Semantic communication is focused on optimizing the exchange of information by transmitting only the most relevant data required to convey the intended message to the receiver and achieve the desired communication goal. For example, if we…
Deep neural networks (DNNs) excel on fixed datasets but struggle with incremental and shifting data in real-world scenarios. Continual learning addresses this challenge by allowing models to learn from new data while retaining previously…
Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit the meaning of information rather than bit-by-bit…
In this paper, we explore semantic clustering properties of deep reinforcement learning (DRL) to improve its interpretability and deepen our understanding of its internal semantic organization. In this context, semantic clustering refers to…
Point cloud, as a 3D representation, is widely used in autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems think that the point cloud's semantic information is irrelevant to…
In existing semantic communication systems for image transmission, some images are generally reconstructed with considerably low quality. As a result, the reliable transmission of each image cannot be guaranteed, bringing significant…
Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer. Aiming to the vision of sixth generation (6G)…
Existing communication systems are mainly built based on Shannon's information theory which deliberately ignores the semantic aspects of communication. The recent iteration of wireless technology, the so-called 5G and beyond, promises to…
The expanding scale of neural networks poses a major challenge for distributed machine learning, particularly under limited communication resources. While split learning (SL) alleviates client computational burden by distributing model…
Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC),…
In task-oriented semantic communications, the transmitters are designed to deliver task-related semantic information rather than every signal bit to receivers, which alleviates the spectrum pressure by reducing network traffic loads.…