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Found 30 papers, 1 papers with code
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.
Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems
In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector.
A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition
Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for non-behavior-based emotion recognition in real-time.
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.
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks.
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications.
Graph Neural Network Aided MU-MIMO Detectors
Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks.
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.
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.
Significant Low-dimensional Spectral-temporal Features for Seizure Detection
Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature.
Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems
Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than $10^{-5}$, when SNR is over $14$ dB, under high ICI environments.
Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation
The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.
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.
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.
Performance Analysis and Optimization of NOMA with HARQ for Short Packet Communications in Massive IoT
The system performance is evaluated for both coordinated and uncoordinated transmissions.
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ~ 50% packet losses).
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.
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.
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.
