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Found 40 papers, 4 papers with code
LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency
The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions.
Diff-GO$^\text{n}$: Enhancing Diffusion Models for Goal-Oriented Communications
The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources.
Plug-in UL-CSI-Assisted Precoder Upsampling Approach in Cellular FDD Systems
Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency-Division Duplexing (FDD).
Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity
To further improve learning performance by increasing device participation under the maximum time consumption constraint, we formulate an energy consumption minimization problem by including resource allocation and sub-channel assignment.
TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation
To characterize radio frequency (RF) signal power distribution in wireless communication systems, the radiomap is a useful tool for resource allocation and network management.
Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback
Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems.
Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection
Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical.
Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency
This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models as a specific example of the general goal-oriented communication framework.
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
Our goal is to optimize DL models such that the encoder latent requires low channel bandwidth while still delivers feature information for high classification accuracy.
Reinforcement Learning for Robust Header Compression under Model Uncertainty
Robust header compression (ROHC), critically positioned between the network and the MAC layers, plays an important role in modern wireless communication systems for improving data efficiency.
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning
Recent advances of generative learning models are accompanied by the growing interest in federated learning (FL) based on generative adversarial network (GAN) models.
Generative Adversarial Network
Personalized Federated Learning
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data
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) has emerged as an important learning paradigm.
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification
Among major deep learning (DL) applications, distributed learning involving image classification require effective image compression codecs deployed on low-cost sensing devices for efficient transmission and storage.
Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map
Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems.
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework.
Low-Complexity Memory AMP Detector for High-Mobility MIMO-OTFS SCMA Systems
Efficient signal detectors are rather important yet challenging to achieve satisfactory performance for large-scale communication systems.
OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications
This paper investigates an uplink coordinated multi-point (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by the remote radio head (RRH) in front of them and the RRH behind them simultaneously.
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
A practical goal is to estimate fine-resolution radio maps from sparse radio strength measurements.
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach
At the leader-level, we derive an upper bound of convergence rate and subsequently reformulate the global loss minimization problem and propose a new age-of-update (AoU) based device selection algorithm.
Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority
Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems.
Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing
To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).
A Scalable Deep Learning Framework for Multi-rate CSI Feedback under Variable Antenna Ports
Channel state information (CSI) at transmitter is crucial for massive MIMO downlink systems to achieve high spectrum and energy efficiency.
Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features.
Signal Processing over Multilayer Graphs: Theoretical Foundations and Practical Applications
To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP).
Image Processing via Multilayer Graph Spectra
Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures.
Over-the-Air Federated Multi-Task Learning
In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).
Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images
The MLA-GDCC includes a white balance (WB) to correct the color imbalance on the images, a modified U-Net and a support vector machine (SVM) to segment the graphene flakes.
An Efficient Hypergraph Approach to Robust Point Cloud Resampling
Efficient processing and feature extraction of largescale point clouds are important in related computer vision and cyber-physical systems.
Point Cloud Resampling Through Hypergraph Signal Processing
By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines.
Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays
In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure.
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback
Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems.
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features.
Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems
Then, we implement the proposed deepunfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems.
Point Cloud Segmentation based on Hypergraph Spectral Clustering
Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis.
Hypergraph Spectral Analysis and Processing in 3D Point Cloud
Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings.
Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications
Furthermore, we show that traditional augmentation transforms such as flipping/shifting are ineffective in the DWT domain and present different augmentation transformations to achieve more accurate classification without any additional cost.
Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference
Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge.
Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications
Signal processing over graphs has recently attracted significant attentions for dealing with structured data.
Federated Learning via Over-the-Air Computation
Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center.
Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow
Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks.
