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Found 110 papers, 8 papers with code
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid
Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors.
Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept
The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform.
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI.
Federated Quantum Long Short-term Memory (FedQLSTM)
The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation.
Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0
In contrast, this paper envisions a novel concept of an Internet of Federated Digital Twins (IoFDT) that holistically integrates heterogeneous and physically separated DTs representing different Society 5. 0 services within a single framework and system.
Rate-Distortion-Perception Theory for Semantic Communication
Motivated by the fact that the semantic information generally involves rich intrinsic knowledge that cannot always be directly observed by the encoder, we consider a semantic information source that can only be indirectly sensed by the encoder.
Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing
Motivated by the observation that signals recorded by wireless receivers are closely related to a set of physical-layer semantic features, in this paper we propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
However, massive data, energy consumption, training complexity, and sensitive data protection in wireless systems are all crucial challenges that must be addressed for training AI models and gathering intelligence and knowledge from distributed devices.
Reasoning with the Theory of Mind for Pragmatic Semantic Communication
In this paper, a pragmatic semantic communication framework that enables effective goal-oriented information sharing between two-intelligent agents is proposed.
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges.
Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing
The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online.
Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?
The generalization and memorization performance of the proposed framework are theoretically analyzed.
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation
For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation.
Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework
Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal.
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges
In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming.
Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences
Second, a non-autoregressive multi-resolution generative artificial intelligence (AI) framework integrated with an adversarial transformer is proposed to predict missing and future sensing information.
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios.
The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities
We then articulate how these experiences bring forth interactions between diverse metaverse constituents, namely, a) humans and avatars and b) connected intelligence systems and their digital twins (DTs).
Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?
Future wireless services, such as the metaverse require high information rate, reliability, and low latency.
Collaborative Learning with a Drone Orchestrator
For this system, the convergence rate of collaborative learning is derived while considering data heterogeneity, sensor noise levels, and communication errors, then, the drone trajectory that maximizes the final accuracy of the trained NN is obtained.
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.
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications
Deep semantic clusters of highest confidence are considered learnable, semantic-rich data, i. e., data that can be used to build a language in a semantic communications system.
Less Data, More Knowledge: Building Next Generation Semantic Communication Networks
In this tutorial, we present the first rigorous vision of a scalable end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, and communication theory.
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities
In this paper, a Transformer-based AI system for emerging smart cities is proposed.
Goal-Oriented Communications for the IoT and Application to Data Compression
Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications.
Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications
In this paper, a novel emergent SC (ESC) system framework is proposed and is composed of a signaling game for emergent language design and a neuro-symbolic (NeSy) artificial intelligence (AI) approach for causal reasoning.
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead.
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.
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach
Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users.
Downlink Interference Analysis of UAV-based mmWave Fronthaul for Small Cell Networks
In this paper, an unmanned aerial vehicles (UAV)-based heterogeneous network is studied to solve the problem of transferring massive traffic of distributed small cells to the core network.
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed.
Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks
This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications.
Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication
Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability.
Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language
In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment.
Positioning Using Visible Light Communications: A Perspective Arcs Approach
In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for VLC-enabled indoor positioning, and a novel perspective arcs approach is proposed.
Edge Continual Learning for Dynamic Digital Twins over Wireless Networks
To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while preserving synchronization between real and digital entities.
Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission
In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate.
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)
In this paper, to characterize the wireless connectivity performance for UAM, a spatial model is proposed.
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.
Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks
To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework
In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution.
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks
In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed.
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning
In federated learning (FL), a machine learning model is trained on multiple nodes in a decentralized manner, while keeping the data local and not shared with other nodes.
A Data-Driven Democratized Control Architecture for Regional Transmission Operators
As probably the most complicated and critical infrastructure system, U. S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users.
Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks
In this paper, a novel framework for proactive caching is proposed.
Ensuring Reliable Connectivity to Cellular-Connected UAVs with Up-tilted Antennas and Interference Coordination
Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas.
Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks
In this paper, a novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks.
Quantum Federated Learning with Quantum Data
First, given the lack of existing quantum federated datasets in the literature, the proposed framework begins by generating the first quantum federated dataset, with a hierarchical data format, for distributed quantum networks.
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Then, we present a detailed literature review on the use of communication techniques for its efficient deployment.
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems
In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied.
Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces
Simulation results show that the error between policies of the optimal and the RNN-based controllers is less than 1. 5%.
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.
Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks
The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains.
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services.
Edge-computing
Networking and Internet Architecture
Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing
Based on these fundamentals, we characterize seven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THz-tailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization.
Information Theory Information Theory
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles
The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.
Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks
To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure.
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs.
Predictive Ultra-Reliable Communication: A Survival Analysis Perspective
Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity.
Survival Analysis
Networking and Internet Architecture
6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities
Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking.
Autonomous Vehicles
Information Theory
Networking and Internet Architecture
Information Theory
Performance Analysis of Age of Information in Ultra-Dense Internet of Things (IoT) Systems with Noisy Channels
Then, a mean-field approximation approach with guaranteed accuracy is developed to analyze the asymptotic performance for the considered system with an infinite number of devices and the effects of the system parameters on the average AoI are characterized.
Information Theory Networking and Internet Architecture Information Theory
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning
Simulation results show the ability of the RL agents to efficiently learn the vehicles' association, RB allocation, and message content selection while maximizing vehicles' satisfaction in terms of the received sensory information.
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem.
Ensuring Data Freshness for Blockchain-enabled Monitoring Networks
The age of information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems where timeliness is of importance.
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV
In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies.
Distributional Reinforcement Learning
reinforcement-learning
+1
Towards Self-learning Edge Intelligence in 6G
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT.
Networking and Internet Architecture
What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?
Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks.
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions
However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models.
Distributed, Parallel, and Cluster Computing
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper.
Delay Minimization for Federated Learning Over Wireless Communication Networks
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
Wireless Communications for Collaborative Federated Learning
However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models.
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks
Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs.
Neurosciences and 6G: Lessons from and Needs of Communicative Brains
In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to 6G in terms of new applications and systems architecture (Neurosciences for Wireless), and the other focused on how wireless communication theory and 6G systems can provide new ways to study the brain (Wireless for Neurosciences).
Signal Processing Emerging Technologies Information Theory Information Theory Neurons and Cognition
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme.
Distributed and Democratized Learning: Philosophy and Research Challenges
Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.
Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videos
In this paper, the problem of head movement prediction for virtual reality videos is studied.
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning
Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10% for online deployments, compared with a Q-learning baseline.
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems
In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent.
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition.
Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets
In this paper, to address this multi-agent GAN problem, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.
Convergence Time Optimization for Federated Learning over Wireless Networks
Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step.
Cellular-Connected Wireless Virtual Reality: Requirements, Challenges, and Solutions
Cellular-connected wireless connectivity provides new opportunities for virtual reality(VR) to offer seamless user experience from anywhere at anytime.
Deep Learning for Optimal Deployment of UAVs with Visible Light Communications
Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution.
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach
Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics.
Deep Learning with Persistent Homology for Orbital Angular Momentum (OAM) Decoding
To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology and convolutional neural networks (CNNs) is proposed to decode the OAM modes.
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
IoT devices with intelligence require the use of effective machine learning paradigms.
Distributed, Parallel, and Cluster Computing
Energy Efficient Federated Learning Over Wireless Communication Networks
To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived.
Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication
Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints.
On the Optimality of Reconfigurable Intelligent Surfaces (RISs): Passive Beamforming, Modulation, and Resource Allocation
In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations.
Information Theory Signal Processing Information Theory
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs
This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users.
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.
Analysis of Memory Capacity for Deep Echo State Networks
Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series.
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things
To this end, in this paper, a distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller.
Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning
For sensors having a prior information, a DIA detection approach is proposed and an optimal threshold level is derived for the difference between the actual and estimated values of sensors data which enables ACV to stay robust against cyber attacks.
Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' locations and orientations.
Information Theory Information Theory
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied.
Information Theory Information Theory
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79\% reductions in the amount of data that need to be exchanged.
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems
To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks.
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users.
Deep Learning for Signal Authentication and Security in Massive Internet of Things Systems
In the massive IoT system, due to a large set of available actions for the cloud, it is shown that analytically deriving the MSNE is challenging and, thus, a learning algorithm proposed that converges to the MSNE.
Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management
The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment.
Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things
In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks.
Information Theory Cryptography and Security Multimedia Information Theory
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment.
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective
LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity.
Stochastic Games for Smart Grid Energy Management with Prospect Prosumers
For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori.
Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks
In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed.
