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Found 25 papers, 4 papers with code
Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs
Semantic- and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task at the receiver.
Rateless Stochastic Coding for Delay-constrained Semantic Communication
We consider the problem of joint source-channel coding with distortion and perception constraints from a rateless perspective, the purpose of which is to settle the balance between reliability (distortion/perception) and effectiveness (rate) of transmission over uncertain channels.
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Rate-Distortion-Perception Theory for Semantic Communication
Motivated by the fact that the semantic information generally involves rich intrinsic knowledge that cannot always be directly observed by the encoder, we consider a semantic information source that can only be indirectly sensed by the encoder.
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.
Analysis and Approximate Inference of Large Random Kronecker Graphs
Random graph models are playing an increasingly important role in various fields ranging from social networks, telecommunication systems, to physiologic and biological networks.
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios.
Mathematical Characterization of Signal Semantics and Rethinking of the Mathematical Theory of Information
Shannon information theory is established based on probability and bits, and the communication technology based on this theory realizes the information age.
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.
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number.
Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning
A federated GCN-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized knowledge datasets.
Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ
Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL).
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.
FedChain: Secure Proof-of-Stake-based Framework for Federated-blockchain Systems
Furthermore, the game model can enhance the security and performance of FedChain.
Computer Science and Game Theory Cryptography and Security
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.
Towards Ubiquitous AI in 6G with Federated Learning
One of the key challenges is the difficulty to implement distributed AI across a massive number of heterogeneous devices.
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning
Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles.
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.
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification
In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch.
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
PDRK: A General Kinetic Dispersion Relation Solver for Magnetized Plasma
A general, fast, and effective approach is developed for numerical calculation of kinetic plasma dispersion relations.
Plasma Physics Solar and Stellar Astrophysics Computational Physics Space Physics
