Search Results for author:
Found 10 papers, 1 papers with code
Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing
Motivated by the observation that signals recorded by wireless receivers are closely related to a set of physical-layer semantic features, in this paper we propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework
Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal.
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach
FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset.
Adversarial Learning for Implicit Semantic-Aware Communications
In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user.
Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges.
Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks
The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains.
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks
The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process.
Towards Self-learning Edge Intelligence in 6G
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network
Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems.
Federated Orchestration for Network Slicing of Bandwidth and Computational Resource
Motivated by the observation that coordination synchronization may result in high coordination delay that can be intolerable when the network is large in scale, we propose a novel asynchronized ADMM algorithm.
Signal Processing Networking and Internet Architecture
