Search Results for author:
Found 70 papers, 6 papers with code
Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation
Text-based communication is expected to be prevalent in 6G applications such as wireless AI-generated content (AIGC).
WVSC: Wireless Video Semantic Communication with Multi-frame Compensation
At the receiver, multi-frame compensation (MFC) is proposed to produce compensated current semantic frame with a multi-frame fusion attention module.
ScNeuGM: Scalable Neural Graph Modeling for Coloring-Based Contention and Interference Management in Wi-Fi 7
Carrier-sense multiple access with collision avoidance in Wi-Fi often leads to contention and interference, thereby increasing packet losses.
SIG-SDP: Sparse Interference Graph-Aided Semidefinite Programming for Large-Scale Wireless Time-Sensitive Networking
Second, the framework designs a matrix multiplicative weights (MMW) algorithm that accelerates the optimization, achieved by only sparsely adjusting interfering user pairs' elements in the PSD matrix while skipping the non-interfering pairs.
Blind Training for Channel-Adaptive Digital Semantic Communications
This strategy makes communication bit errors align with the pre-trained bit-flip probabilities by adaptively selecting power and modulation levels based on practical requirements and channel conditions.
Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models
This paper studies a hybrid language model (HLM) architecture that integrates a small language model (SLM) operating on a mobile device with a large language model (LLM) hosted at the base station (BS) of a wireless network.
Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder
Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance.
Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity
In particular, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of 6DMA channels, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from users.
Koopman AutoEncoder via Singular Value Decomposition for Data-Driven Long-Term Prediction
The Koopman autoencoder, a data-driven technique, has gained traction for modeling nonlinear dynamics using deep learning methods in recent years.
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
In computer vision, the vision transformer (ViT) has increasingly superseded the convolutional neural network (CNN) for improved accuracy and robustness.
Rethinking Multi-User Semantic Communications with Deep Generative Models
In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing.
Language-Oriented Semantic Latent Representation for Image Transmission
In the new paradigm of semantic communication (SC), the focus is on delivering meanings behind bits by extracting semantic information from raw data.
Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks
In this article, we propose a multi-agent deep reinforcement learning (MADRL) framework to train a multiple access protocol for downlink low earth orbit (LEO) satellite networks.
Graph Koopman Autoencoder for Predictive Covert Communication Against UAV Surveillance
Low Probability of Detection (LPD) communication aims to obscure the very presence of radio frequency (RF) signals, going beyond just hiding the content of the communication.
Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control
In this work, we compare emergent communication (EC) built upon multi-agent deep reinforcement learning (MADRL) and language-oriented semantic communication (LSC) empowered by a pre-trained large language model (LLM) using human language.
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago.
Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches
neural network-oriented symbolic protocols developed by converting Level 1 MAC outputs into explicit symbols; and Level 3 MAC.
Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication
In this method, referred to as SL with layer freezing (SLF), each encoder downloads a misaligned decoder, and locally fine-tunes a fraction of these encoder-decoder NN layers.
Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation
By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC).
Ranked #1 on
Text-to-Image Generation
on Flickr-8k
Sequential Semantic Generative Communication for Progressive Text-to-Image Generation
Our work is expected to pave a new road of utilizing state-of-the-art generative models to real communication systems
Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators
In this paper, we introduce a novel semantic generative communication (SGC) framework, where generative users leverage text-to-image (T2I) generators to create images locally from downloaded text prompts, while non-generative users directly download images from a base station (BS).
Energy-Efficient UAV-Assisted IoT Data Collection via TSP-Based Solution Space Reduction
This paper presents a wireless data collection framework that employs an unmanned aerial vehicle (UAV) to efficiently gather data from distributed IoT sensors deployed in a large area.
Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks
Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks.
CSI-Inpainter: Enabling Visual Scene Recovery from CSI Time Sequences for Occlusion Removal
Introducing CSI-Inpainter, a pioneering approach for occlusion removal using Channel State Information (CSI) time sequences, this work propels the application of wireless signal processing into the realm of visual scene recovery.
A Subspace Projection Approach to Autoencoder-based Anomaly Detection
Autoencoder (AE) is a neural network (NN) architecture that is trained to reconstruct an input at its output.
Enabling the Wireless Metaverse via Semantic Multiverse Communication
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements.
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study.
Differentially Private CutMix for Split Learning with Vision Transformer
Split learning (SL) detours this by communicating smashed data at a cut-layer, yet suffers from data privacy leakage and large communication costs caused by high similarity between ViT' s smashed data and input data.
Quantum Multi-Agent Meta Reinforcement Learning
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing.
Slimmable Quantum Federated Learning
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL).
Towards Semantic Communication Protocols: A Probabilistic Logic Perspective
Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications.
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Leveraging this, we develop a novel SL framework for ViT, coined CutMixSL, communicating CutSmashed data.
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design
This paper extends and demonstrates the QRL to quantum multi-agent RL (QMARL).
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Attention Based Communication and Control for Multi-UAV Path Planning
Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning.
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
In recent years, there have been great advances in the field of decentralized learning with private data.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.
Learning Emergent Random Access Protocol for LEO Satellite Networks
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems.
Semantics-Native Communication with Contextual Reasoning
Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks.
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.
Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations
In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated.
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples.
Communication-Efficient and Personalized Federated Lottery Ticket Learning
The lottery ticket hypothesis (LTH) claims that a deep neural network (i. e., ground network) contains a number of subnetworks (i. e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network.
Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction
Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus
Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data.
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer.
Federated Knowledge Distillation
The goal of this chapter is to provide a deep understanding of FD while demonstrating its communication efficiency and applicability to a variety of tasks.
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks
Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62. 56x higher peak throughput and 21. 09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5G NTNs.
When Wireless Communications Meet Computer Vision in Beyond 5G
This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications).
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
RIS-Assisted Coverage Enhancement in Millimeter-Wave Cellular Networks
The use of millimeter-wave (mmWave) bandwidth is one key enabler to achieve the high data rates in the fifth-generation (5G) cellular systems.
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth.
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent.
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory
Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions.
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM).
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).
Remote UAV Online Path Planning via Neural Network Based Opportunistic Control
This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB.
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).
Federated Reinforcement Distillation with Proxy Experience Memory
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models.
Multi-hop Federated Private Data Augmentation with Sample Compression
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity.
Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach
Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations.
Wireless Network Intelligence at the Edge
), requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML).
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples.
Blockchained On-Device Federated Learning
By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified.
Information Theory Networking and Internet Architecture Information Theory
