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Found 41 papers, 5 papers with code
A Robust Semantic Communication System for Image
Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques.
Robust Semantic Communications for Speech-to-Text Translation
Moreover, to cope with the practical communication scenario when the input speech is corrupted, a novel generative adversarial network (GAN)-enabled deep semantic compensator is proposed to predict the lost semantic information in the source speech and produce the textual semantic features in the target language simultaneously, which establishes a robust semantic transmission mechanism for dynamic speech input.
Generative Adversarial Network
Speech-to-Text Translation
+1
Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks
To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer non-linear programming (MINLP) problem.
Towards Intelligent Communications: Large Model Empowered Semantic Communications
First, we propose a new semantic communication architecture that seamlessly integrates large models into semantic communication through the introduction of a memory module.
Federated Multi-View Synthesizing for Metaverse
With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse.
Adaptive Resource Allocation for Semantic Communication Networks
In order to investigate the performance of semantic communication networks, the quality of service for semantic communication (SC-QoS), including the semantic quantization efficiency (SQE) and transmission latency, is proposed for the first time.
An End-Cloud Computing Enabled Surveillance Video Transmission System
The enormous data volume of video poses a significant burden on the network.
Compression Ratio Learning and Semantic Communications for Video Imaging
In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself.
Meta Federated Reinforcement Learning for Distributed Resource Allocation
In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead.
Semantic-Aware Image Compressed Sensing
According to the base CS results, the encoder then employs a policy network to analyze the semantic information in images and determines the measurement matrix for different image areas.
Semantic Communication with Memory
While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications.
Semantic Communications with Variable-Length Coding for Extended Reality
Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience.
Resource Optimization for Semantic-Aware Networks with Task Offloading
Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution.
A Generalized Semantic Communication System: from Sources to Channels
This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment.
Semantic Sensing and Communications for Ultimate Extended Reality
In particular, semantic sensing is used to improve the sensing efficiency by exploring the spatial-temporal distributions of semantic information.
Semantic Communication for Internet of Vehicles: A Multi-User Cooperative Approach
Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security.
Vector Quantized Semantic Communication System
Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems.
A Unified Multi-Task Semantic Communication System for Multimodal Data
Task-oriented semantic communications have achieved significant performance gains.
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences.
Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated.
A Robust Deep Learning Enabled Semantic Communication System for Text
With the advent of the 6G era, the concept of semantic communication has attracted increasing attention.
A Unified Multi-Task Semantic Communication System with Domain Adaptation
Simulation results demonstrate that our proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task with significant transmission overhead reduction and much less number of model parameters.
QoE-Aware Resource Allocation for Semantic Communication Networks
Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed.
Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis
In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively.
Federated Learning for Distributed Energy-Efficient Resource Allocation
Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
Robust Semantic Communications Against Semantic Noise
In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise.
Resource allocation for text semantic communications
Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime.
Semantic Communications: Principles and Challenges
Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning.
Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems
The vigorous developments of Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL).
Task-Oriented Multi-User Semantic Communications
For the single-modal multi-user system, we will propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively.
Task-Oriented Multi-User Semantic Communications for VQA Task
In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission.
Semantic Communications for Speech Recognition
The traditional communications transmit all the source date represented by bits, regardless of the content of source and the semantic information required by the receiver.
Reconfigurable Intelligent Surface Assisted Device-to-Device Communications
Reconfigurable intelligent surface (RIS) technology is a promising method to enhance wireless communications services and to realize the smart radio environment.
Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis
We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis.
Semantic Communication Systems for Speech Transmission
In order to improve the recovery accuracy of speech signals, especially for the essential information, DeepSC-S is developed based on an attention mechanism by utilizing a squeeze-and-excitation (SE) network.
Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks
Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks.
Semantic Communications for Speech Signals
We consider a semantic communication system for speech signals, named DeepSC-S.
A Lite Distributed Semantic Communication System for Internet of Things
To make it affordable for IoT devices, we propose a lite distributed semantic communication system based on DL, named L-DeepSC, for text transmission with low complexity, where the data transmission from the IoT devices to the cloud/edge works at the semantic level to improve transmission efficiency.
Reconfigurable Intelligent Surface Enhanced Device-to-Device Communications
Reconfigurable intelligent surface (RIS) technology is a promising method to enhance the device-to-device (D2D) communications.
Deep Learning Enabled Semantic Communication Systems
To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity.
Federated Learning and Wireless Communications
In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model.
Information Theory Signal Processing Information Theory
