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Found 23 papers, 5 papers with code
DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming
This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks.
Joint Devices and IRSs Association for Terahertz Communications in Industrial IoT Networks
The Industrial Internet of Things (IIoT) enables industries to build large interconnected systems utilizing various technologies that require high data rates.
Channel Characterization of UAV-RIS-aided Systems with Adaptive Phase-shift Configuration
This letter considers a UAV aiding communication between a ground transmitter and a ground receiver in the presence of co-channel interference.
User Association Optimization for IRS-aided Terahertz Networks: A Matching Theory Approach
Terahertz (THz) communication is a promising technology for future wireless communications, offering data rates of up to several terabits-per-second (Tbps).
Statistical Characterization of RIS-assisted UAV Communications in Terrestrial and Non-Terrestrial Networks Under Channel Aging
This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications with unmanned aerial vehicles (UAVs) in terrestrial and non-terrestrial networks under the impact of channel aging.
Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study
At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler by promising to minimize bandwidth consumption, transmission delay, and power usage.
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
Although deep learning (DL) has led to several breakthroughs in many disciplines as diverse as chemistry, computer science, electrical engineering, mathematics, medicine, neuroscience, and physics, a comprehensive understanding of why and how DL is empirically successful remains fundamentally elusive.
Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication
Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges.
Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission -- minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise.
Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review
In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed.
Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies
Moreover, when 70 % of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves an STR and ER greater than 75 % and 80 %, respectively.
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem
In this paper, we address a collision scenario, called Outside Warning Range Problem (OWRP), that may occur during DBCA when a wireless station interferes with another wireless station after channel bonding is performed.
Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach
In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes.
Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach
To evaluate the performance of the proposed recognition method, the accuracy of the detection is derived as a function of the jammer policy switching time.
Designing a Pseudo-Random Bit Generator with a Novel 5D-Hyperchaotic System
Moreover, a data scrambling circuit is implemented to eliminate the bias effect and increase the randomness of the bitstream generated from the chaotic signals.
Deep Chaos Synchronization
In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN).
Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks
Different from existing works, in this paper, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels.
Random Fourier Feature Based Deep Learning for Wireless Communications
Deep-learning (DL) has emerged as a powerful machine-learning technique for several classic problems encountered in generic wireless communications.
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog Environment
In this paper, we propose FID-GAN, a novel fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs.
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks
In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing.
Lightwave Power Transfer for Federated Learning-based Wireless Networks
Hence, the proposed approach can support the FL-based wireless network to overcome the issue of limited energy in mobile devices.
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning
Many solutions proposed in the literature are reported to have high accuracy but are ineffective in real applications due to the non-representativity of the dataset used for training and evaluation of the underlying models.
Robust Design of AC Computing-Enabled Receiver Architecture for SWIPT Networks
Inspired by the direct use of alternating current (AC) for computation, we propose a novel integrated information and energy receiver architecture for simultaneous wireless information and power transfer (SWIPT) networks.
Information Theory Information Theory
