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Found 40 papers, 1 papers with code
Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approach
In addressing wireless networked control systems (WNCS) subject to unexpected packet loss and uncertainties, this paper presents a practical Model Predictive Control (MPC) based control scheme with considerations of of packet dropouts, latency, process noise and measurement noise.
Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation
To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training.
Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications
This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper.
Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks
Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements.
GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection
It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning.
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance.
Hybrid NOMA assisted Integrated Sensing and Communication via RIS
Furthermore, the trade-off between sensing and communication is analyzed and demonstrated in the simulation results.
Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach
For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance.
Structure-Enhanced DRL for Optimal Transmission Scheduling
In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.
Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling
Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure.
A Scalable Graph Neural Network Decoder for Short Block Codes
In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN).
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks
Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals.
Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach
Wireless networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost deployment of control systems in the Industry 4. 0 era.
Unitary Approximate Message Passing for Matrix Factorization
We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas.
Reconfigurable Intelligent Surface-aided $M$-ary FM-DCSK System: a New Design for Noncoherent Chaos-based Communication
In this paper, we propose two reconfigurable intelligent surface-aided $M$-ary frequency-modulated differential chaos shift keying (RIS-$M$-FM-DCSK) schemes.
DRL-based Resource Allocation in Remote State Estimation
Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings.
Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network
This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network.
Stability Conditions for Remote State Estimation of Multiple Systems over Semi-Markov Fading Channels
In particular, we show that, from a system stability perspective, fast fading channels may be preferable to slow fading ones.
Proximal Policy Optimization-based Transmit Beamforming and Phase-shift Design in an IRS-aided ISAC System for the THz Band
Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario.
Weighted Sum Age of Information Minimization in Wireless Networks with Aerial IRS
In this letter, we analyze a terrestrial wireless communication network assisted by an aerial intelligent reflecting surface (IRS).
NOMA Joint Channel Estimation and Signal Detection using Rotational Invariant Codes and GMM-based Clustering
This paper studies the joint channel estimation and signal detection for the uplink power-domain non-orthogonal multiple access.
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control
We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels.
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.
Elevation Angle-Dependent 3D Trajectory Design for Aerial RIS-aided Communication
This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange.
Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface
This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS).
Stability Conditions for Remote State Estimation of Multiple Systems over Multiple Markov Fading Channels
We investigate the stability conditions for remote state estimation of multiple linear time-invariant (LTI) systems over multiple wireless time-varying communication channels.
A New Frequency-Bin-Index LoRa System for High-Data-Rate Transmission: Design and Performance Analysis
To further improve the system flexibility, we formulate a generalized modulation scheme and propose scheme II by treating the SFB groups as an additional type of transmission entity.
Catalytically Potent and Selective Clusterzymes for Modulation of Neuroinflammation Through Single-Atom Substitutions
Emerging artificial enzymes with reprogrammed and augmented catalytic activity and substrate selectivity have long been pursued with sustained efforts.
Biological Physics Medical Physics
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Secret Key Generation for Intelligent Reflecting Surface Assisted Wireless Communication Networks
We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks.
Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles
We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint
We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC).
Information Theory Networking and Internet Architecture Signal Processing Information Theory
Physical Layer Authentication for Non-Coherent Massive SIMO-Enabled Industrial IoT Communications
This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as "message-based tag embedding".
Remote State Estimation with Smart Sensors over Markov Fading Channels
A smart sensor forwards its local state estimate to a remote estimator over a time-correlated $M$-state Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state.
Reconfigurable Intelligent Surface (RIS)-Enhanced Two-Way OFDM Communications
In this paper, we focus on the reconfigurable intelligent surface (RIS)-enhanced two-way device-to-device (D2D) multi-pair orthogonal-frequency-division-multiplexing (OFDM) communication systems.
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in the 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions.
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.
Crowd Scene Analysis by Output Encoding
In addition, proper receptive field sizes are crucial for crowd analysis due to human size variations.
A Learning-Based Two-Stage Spectrum Sharing Strategy with Multiple Primary Transmit Power Levels
Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.
