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Found 27 papers, 0 papers with code
DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs
Unlike the existing jamming detection algorithms that mostly rely on network parameters, we introduce a double-threshold deep learning jamming detector by focusing on the SSB.
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks
To this end, we leverage attention mechanisms as a defense against attacks in FL and propose a robust FL algorithm by integrating the attention mechanisms into the global model aggregation step.
Adversarial Machine Learning-Enabled Anonymization of OpenWiFi Data
Data privacy and protection through anonymization is a critical issue for network operators or data owners before it is forwarded for other possible use of data.
Telecom AI Native Systems in the Age of Generative AI -- An Engineering Perspective
The rapid advancements in Artificial Intelligence (AI), particularly in generative AI and foundational models (FMs), have ushered in transformative changes across various industries.
Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning
In this paper, we propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes.
Cooperative Hierarchical Deep Reinforcement Learning based Joint Sleep, Power, and RIS Control for Energy-Efficient HetNet
In this work, we jointly consider sleep and transmission power control for reconfigurable intelligent surface (RIS)-aided energy-efficient heterogeneous networks (Hetnets).
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks
Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques.
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs
The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm.
Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning
Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test and is closer to the maximum theoretical EE.
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning
Federated Learning (FL) has emerged as a promising framework for distributed training of AI-based services, applications, and network procedures in 6G.
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity
Reinforcement learning (RL) has shown its huge potential in wireless scenarios where parameter learning is required given the dynamic nature of such context.
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN
Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks.
The Internet of Senses: Building on Semantic Communications and Edge Intelligence
On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents.
Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul
The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture
IoT devices have limited energy and computing resources, thus it is required to provide an advanced solution for a system that requires the support of ABSs.
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks
UAVs have limited energy and computing power, and may not be able to perform all of the intense image classification tasks locally and within an appropriate amount of time.
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G
Then we propose a UK-medoids based method for user clustering with location uncertainty, and the clustering results are consequently used for the beam management.
Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks
In this paper, we propose a UK-means-based clustering and deep reinforcement learning-based resource allocation algorithm (UK-DRL) for radio resource allocation and beam management in 5G mmWave networks.
Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home
To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase. We compared the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model.
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures
Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid.
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning
A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns.
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing
In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing.
Risk-Aware Fine-Grained Access Control in Cyber-Physical Contexts
The risk levels are associated with access control decisions recommended by a security policy.
Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning
Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities.
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning
In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions and jointly minimize VNF cost and AoI in terms of network resources under end-to end Quality of Service constraints.
Correlated Deep Q-learning based Microgrid Energy Management
Microgrid (MG) energy management is an important part of MG operation.
Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach
The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated.
