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Found 38 papers, 3 papers with code
Digital Deep Joint Source-Channel Coding with Blind Training for Adaptive Modulation and Power Control
To address this challenge, in our framework, an error-adaptive blind training strategy is devised, which eliminates the need for prior knowledge of communication environments.
Sparse autoencoders reveal selective remapping of visual concepts during adaptation
Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications.
Adaptive Power Allocation in Spaceborne Assisted NOMA Systems for Integrated Terrestrial Communications
This study introduces an innovative approach for adaptive power allocation in Non-Orthogonal Multiple Access (NOMA) systems, enhanced by the integration of spaceborne and terrestrial signals through a Reconfigurable Intelligent Surface (RIS).
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.
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.
Iterative Sparse Identification of Nonlinear Dynamics
Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing.
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.
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).
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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.
Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios
The proposed CKD framework transfers and fuses pose knowledge from a robust "Teacher" model to a parameterized "Student" model, which can be a promising technique for obtaining accurate yet lightweight pose estimates.
FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems
In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal.
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.
Poisoning Attacks and Defenses in Federated Learning: A Survey
Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats.
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.
Joint Design of Power Control and Access Point Scheduling for Uplink Cell-Free Massive MIMO Networks
It is demonstrated that the proposed bound provides a more accurate performance estimate of CF-mMIMO than that of the existing UatF bound.
Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP
The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL.
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.
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
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.
Enhancing Cognitive Models of Emotions with Representation Learning
We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions that can be used to computationally describe psychological models of emotions.
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.
Modeling RIS Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks
With the increasing adoption of millimeter-waves (mmWave) over cellular networks, outdoor-to-indoor (O2I) communication has been one of the challenging research problems due to high penetration loss of buildings.
Data-aided Sensing for Gaussian Process Regression in IoT Systems
In this paper, for efficient data collection with limited bandwidth, data-aided sensing is applied to Gaussian process regression that is used to learn data sets collected from sensors in Internet-of-Things systems.
Data-aided Sensing for Distributed Detection
In this paper, we study data-aided sensing (DAS) for distributed detection in wireless sensor networks (WSNs) when sensors' measurements are correlated.
Subcarrier-wise Backscatter Communications over Ambient OFDM for Low Power IoT
Analytical and numerical evaluations provide a proof to see the performance of the proposed method in terms of BER, data rate, and interference.
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.
Short-Range Ambient Backscatter Communication Using Reconfigurable Intelligent Surfaces
In this paper, we propose a new AmBC model over ambient orthogonal-frequency-division-multiplexing (OFDM) subcarriers in the frequency domain in conjunction with RIS for short-range communication scenarios.
Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO
Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA).
Federated learning with multichannel ALOHA
In this paper, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets.
Information Theory Signal Processing Information Theory
