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Found 17 papers, 0 papers with code
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces
Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels.
Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning.
Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access
Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not.
Joint Sensing and Semantic Communications with Multi-Task Deep Learning
The transmitter employs a deep neural network, namely an encoder, for joint operations of source coding, channel coding, and modulation, while the receiver utilizes another deep neural network, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples.
Semantic Text Compression for Classification
We study semantic compression for text where meanings contained in the text are conveyed to a source decoder, e. g., for classification.
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning
A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers.
Age of Information in Deep Learning-Driven Task-Oriented Communications
This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter.
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks
The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning.
Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks
By augmenting the reconstruction loss with a semantic loss, the two deep neural networks (DNNs) of this encoder-decoder pair are interactively trained with the DNN of the semantic task classifier.
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects
In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences.
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device.
Secure Joint Communication and Sensing
For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed.
Autoencoder-based Communications with Reconfigurable Intelligent Surfaces
This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-to-end communication performance at the receiver.
Sustainable Federated Learning
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems.
Energy-Harvesting Distributed Machine Learning
This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks.
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks.
