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Found 56 papers, 5 papers with code
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Multi-Modal Environmental Sensing Based Path Loss Prediction for V2I Communications
The stability and reliability of wireless data transmission in vehicular networks face significant challenges due to the high dynamics of path loss caused by the complexity of rapidly changing environments.
New Characteristics and Modeling of 6G Channels: A Unified Channel Model towards Standardization
As 6G research advances, the growing demand leads to the emergence of novel technologies such as Integrated Sensing and Communication (ISAC), new antenna arrays like Extremely Large MIMO (XL-MIMO) and Reconfigurable Intelligent Surfaces (RIS), along with multi-frequency bands (6-24 GHz, above 100 GHz).
Environment Reconstruction with Multi-targets Reflectors-merged Sensing Method Based on THz Single-sided Channel Characteristics
Reconstructing the propagation environment is a vital step for THz ISAC, as it enhances the predictability of the communication channel to reduce communication overhead.
ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence
In practice, we also establish a ChannelGPT prototype to generate high-fidelity channel data for varied scenarios to validate the accuracy and generalization ability based on environment intelligence.
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
In federated learning, the heterogeneity of client data has a great impact on the performance of model training.
BUPTCMCC-6G-CMG+: A GBSM-Based ISAC Standard Channel Model Generator
Integrated sensing and communication (ISAC) has been recognized as the key technology in the vision of the sixth generation (6G) era.
Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example
Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI.
Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions
Specifically, the user equipment (UE) extracts the primary stochastic parameters of the field channel data and transmits them to the base station (BS).
An Approximate Wave-Number Domain Expression for Near-Field XL-MIMO Channel
As Extremely Large-Scale Multiple-Input-Multiple-Output (XL-MIMO) technology advances and carrier frequency rises, the near-field effects in communication are intensifying.
Pandora: Towards General World Model with Natural Language Actions and Video States
This paper makes a step towards building a general world model by introducing Pandora, a hybrid autoregressive-diffusion model that simulates world states by generating videos and allows real-time control with free-text actions.
Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph
The task of reasoning over Knowledge Graphs (KGs) poses a significant challenge for Large Language Models (LLMs) due to the complex structure and large amounts of irrelevant information.
FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Flight Trajectory Prediction
To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction.
Pseudo MIMO (pMIMO): An Energy and Spectral Efficient MIMO-OFDM System
The proposed system architecture and the dedicated signal processing methods enable the scheme to transmit a bigger number of parallel data streams than the number of receiving RF chains, achieving a spectral efficiency performance close to that of a fully digital (FD) MIMO system with the same number of antenna elements, each equipped with an RF chain.
Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences.
Digital Twin Channel for 6G: Concepts, Architectures and Potential Applications
Digital twin channel (DTC) is the real-time mapping of a wireless channel from the physical world to the digital world, which is expected to provide significant performance enhancements for the sixth-generation (6G) air-interface design.
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images.
Risk of Cascading Collisions in Network of Vehicles with Delayed Communication
This paper establishes and explores a framework to analyze the risk of cascading failures in a platoon of autonomous vehicles, accounting for communication time-delays and input uncertainty.
Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool
The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions.
How to Extend 3D GBSM to Integrated Sensing and Communication Channel with Sharing Feature?
The proposed approach can be summarized as follows: Firstly, an ISAC channel model is proposed, where shared and non-shared components are superimposed for both communication and sensing.
Quantification of Distributionally Robust Risk of Cascade of Failures in Platoon of Vehicles
In this paper, we propose a distributionally robust risk framework to investigate cascading failures in platoons.
Channel Measurement, Modeling, and Simulation for 6G: A Survey and Tutorial
Then, a survey of the progress of the 6G channel research regarding the above five promising technologies is presented in terms of the latest measurement campaigns, new characteristics, modeling methods, and research prospects.
Multistatic Integrated Sensing and Communication System in Cellular Networks
A novel multistatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system in cellular networks is proposed.
Multi-User Matching and Resource Allocation in Vision Aided Communications
Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system.
Symbolic Perception Risk in Autonomous Driving
We develop a novel framework to assess the risk of misperception in a traffic sign classification task in the presence of exogenous noise.
Cascading Waves of Fluctuation in Time-delay Multi-agent Rendezvous
We develop a framework to assess the risk of cascading failures when a team of agents aims to rendezvous in time in the presence of exogenous noise and communication time-delay.
How to Extend 3D GBSM to RIS Cascade Channel with Non-ideal Phase Modulation?
In the modeling process, we consider the non-ideal phase modulation of the RIS element, so as to accurately characterize the dependence of its phase modulation on the incoming wave angle.
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection
In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images.
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction
In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks.
A Generalized Semantic Communication System: from Sources to Channels
This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment.
Capacity Analysis of Holographic MIMO Channels with Practical Constraints
However, the channel might not satisfy isotropic scattering because of generalized angle distributions, and the antenna gain is limited by the array aperture in reality.
A Shared Cluster-based Stochastic Channel Model for Integrated Sensing and Communication Systems
Then, a stochastic ISAC channel model is proposed to capture the sharing feature, where shared and non-shared clusters by the two channels are dfined and superimposed.
Composable Text Controls in Latent Space with ODEs
This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
Ranked #2 on
Unsupervised Text Style Transfer
on Yelp
Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X
In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair.
Frequency-Angle Two-Dimensional Reflection Coefficient Modeling Based on Terahertz Channel Measurement
In this letter, the reflection coefficient of the THz channel is researched based on extensive measurement campaigns.
A Robust Deep Learning Enabled Semantic Communication System for Text
With the advent of the 6G era, the concept of semantic communication has attracted increasing attention.
Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis
In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively.
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
Emergence of Cascading Risk and Role of Spatial Locations of Collisions in Time-Delayed Platoon of Vehicles
We develop a framework to assess the risk of cascading collisions in a platoon of vehicles in the presence of exogenous noise and communication time-delay.
Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks
We develop a framework to quantify systemic risk measures in a class of Wide-Area-Control (WAC) laws in power networks in the presence of noisy and time-delayed sensory data.
Risk of Cascading Failures in Time-Delayed Vehicle Platooning
We develop a systemic risk framework to explore cascading systemic failures in networked control systems.
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
Using Terminal Circuit for Power System Electromagnetic Transient Simulation
The modern power system is evolving with increasing penetration of power electronics introducing complicated electromagnetic phenomenon.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
An Intelligent Question Answering System based on Power Knowledge Graph
The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user.
Power System Transient Modeling and Simulation using Integrated Circuit
Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system.
Robust Learning of Recurrent Neural Networks in Presence of Exogenous Noise
However, artificial neural networks are known to exhibit poor robustness in presence of input noise, where the sequential architecture of RNNs exacerbates the problem.
Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability
A total 19% of generation capacity in California is offered by PV units and over some months, more than 10% of this energy is curtailed.
Distributed Map Classification using Local Observations
We consider the problem of classifying a map using a team of communicating robots.
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning
We propose a planning and perception mechanism for a robot (agent), that can only observe the underlying environment partially, in order to solve an image classification problem.
Enhancement of Power Equipment Management Using Knowledge Graph
Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process.
Power Market Price Forecasting via Deep Learning
Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes.
Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification
Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels.
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting
In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph.
Urban MV and LV Distribution Grid Topology Estimation via Group Lasso
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid.
