Related papers: D$^2$-JSCC: Digital Deep Joint Source-channel Codi…
We propose an adaptive lossy joint source-channel coding (JSCC) scheme for sending correlated sources over two-terminal discrete-memoryless two-way channels (DM-TWCs). The main idea is to couple the independent operations of the terminals…
Semantic Communication (SemCom) has emerged as a promising paradigm for 6G networks, aiming to extract and transmit task-relevant information rather than minimizing bit errors. However, applying SemCom to realistic downlink Multi-User…
The traditional communications transmit all the source data represented by bits, regardless of the content of source and the semantic information required by the receiver. However, in some applications, the receiver only needs part of the…
Semantic communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data. In this paper, in order to tackle the…
Significant progress has been made in wireless Joint Source-Channel Coding (JSCC) using deep learning techniques. The latest DL-based image JSCC methods have demonstrated exceptional performance during transmission, while also avoiding…
Discretization of semantic features enables interoperability between semantic and digital communication systems, showing significant potential for practical applications. The fundamental difficulty in digitizing semantic features stems from…
Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust semantic features can be learned and transmitted in an analog…
Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources. We propose a deep JSCC scheme for wireless…
The semantic information of the image for intelligent tasks is hidden behind the pixels, and slight changes in the pixels will affect the performance of intelligent tasks. In order to preserve semantic information behind pixels for…
Semantic communication, enabled by deep joint source-channel coding (DeepJSCC), is widely expected to inherit the vulnerability of deep learning to adversarial perturbations. This paper challenges this prevailing belief and reveals a…
We present an AI-based framework for semantic transmission of multimedia data over band-limited, time-varying channels. The method targets scenarios where large content is split into multiple packets, with an unknown number potentially…
Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication…
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 deep learning (DL) techniques. Specifically, SemCom can…
In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered significant attention from both academia and industry. To ensure the security of…
Semantic knowledge bases are regarded as a promising technology for upcoming 6G communications. However, existing studies mainly focus on source-side semantic modeling while overlooking the structural impact of propagation environments on…
Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of…
As semantic communication (SemCom) attracts growing attention as a novel communication paradigm, ensuring the security of transmitted semantic information over open wireless channels has become a critical issue. However, traditional…
We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first…
Most of current semantic communication (SemCom) frameworks focus on the image transmission, which, however, do not address the problem on how to deliver digital signals without any semantic features. This paper proposes a novel SemCom…
Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware…