Search Results for author:
Found 39 papers, 4 papers with code
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems.
Semantic Communication with Entropy-and-Channel-Adaptive Rate Control
Traditional wireless image transmission methods struggle to balance rate efficiency and reconstruction quality under varying channel conditions.
Implicit Neural Compression of Point Clouds
Our approach employs two coordinate-based neural networks to implicitly represent a voxelized point cloud: the first determines the occupancy status of a voxel, while the second predicts the attributes of occupied voxels.
CASC: Condition-Aware Semantic Communication with Latent Diffusion Models
By operating in the latent space, the LDM reduces computational complexity compared to traditional diffusion models (DMs).
Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM).
Object-Attribute-Relation Representation Based Video Semantic Communication
In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission.
Semantic Communication for Edge Intelligence Enabled Autonomous Driving System
Meanwhile, the emergence of edge intelligence has further introduced significant advancements to this field.
Point Cloud Compression with Implicit Neural Representations: A Unified Framework
By feeding the coordinates of these voxels into the respective networks, we reconstruct the geometry and attribute components of the original point cloud.
Enhancing Image Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy
Semantic communication (SemCom) enhances transmission efficiency by sending only task-relevant information compared to traditional methods.
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
FeDeRA follows LoRA by decomposing the weight matrices of the PLMs into low-rank matrices, which allows for more efficient computation and parameter updates during fine-tuning.
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images).
Evolving Semantic Communication with Generative Model
In this paper, we explore an evolving semantic communication system for image transmission, referred to as ESemCom, with the capability to continuously enhance transmission efficiency.
Point Cloud in the Air
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality.
Deep Learning Enabled Semantic Communication Systems for Video Transmission
Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks.
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks.
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training.
The Model Inversion Eavesdropping Attack in Semantic Communication Systems
In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency.
Semantic-aware Transmission for Robust Point Cloud Classification
As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control
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.
MIMO Precoding Design with QoS and Per-Antenna Power Constraints
Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem.
Semantic-aware Digital Twin for Metaverse: A Comprehensive Review
To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic awareness has been proposed as a means for enabling accurate and task-oriented information extraction with inherent intelligence.
Robust, fast and accurate mapping of diffusional mean kurtosis
In summary, our findings suggest robust, fast and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion weighted magnetic resonance imaging data acquisition time.
Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission
Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years.
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation
In an asynchronous federated learning framework, the server updates the global model once it receives an update from a client instead of waiting for all the updates to arrive as in the synchronous setting.
Semantic-preserved Communication System for Highly Efficient Speech Transmission
Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years.
OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt
In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.
A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis
18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the potential health risks of radiation exposure, especially for pediatric patients.
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
In particular, the proposed model includes a multilevel semantic feature extractor, that extracts both the highlevel semantic information, such as the text semantics and the segmentation semantics, and the low-level semantic information, such as local spatial details of the images.
Semantic-aware Speech to Text Transmission with Redundancy Removal
We also propose a two-stage training scheme, which speeds up the training of the proposed DL model.
Blind Channel Estimation for MIMO Systems via Variational Inference
In this paper, we investigate the blind channel estimation problem for MIMO systems under Rayleigh fading channel.
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks
In this paper, a joint model split and neural architecture search (JMSNAS) framework is proposed to automatically generate and deploy a DNN model over a mobile edge network.
FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devices
Our numerical results demonstrate that FTPipeHD is 6. 8x faster in training than the state of the art method when the computing capacity of the best device is 10x greater than the worst one.
Communication-Efficient Federated Learning with Binary Neural Networks
{ Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.
RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection
The novel 2019 Coronavirus (COVID-19) infection has spread world widely and is currently a major healthcare challenge around the world.
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback
We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner.
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems
Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains.
Deep Convolutional Compression for Massive MIMO CSI Feedback
In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS.
The Multi-layer Information Bottleneck Problem
Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variable, quantified by the mutual information.
