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Found 119 papers, 10 papers with code
Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation
By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC).
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption.
Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures
Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations.
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.
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction
Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored.
Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication
This work deals with the heterogeneous semantic-native communication (SNC) problem.
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations
Equivalently, this corresponds to a game played between multiple RISs, as learning agents, in heterogeneous environments.
Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition
This paper studies the computational offloading of video action recognition in edge computing.
Trans-Inpainter: Wireless Channel Information- Guided Image Restoration via Multimodal Transformer
Image inpainting is a critical computer vision task to restore missing or damaged image regions.
Codesign of Edge Intelligence and Automated Guided Vehicle Control
This work presents a harmonic design of autonomous guided vehicle (AGV) control, edge intelligence, and human input to enable autonomous transportation in industrial environments.
Performance Analysis of ML-based MTC Traffic Pattern Predictors
Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost.
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.
Computation and Privacy Protection for Satellite-Ground Digital Twin Networks
Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users.
Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
To shed light on this knowledge gap and stimulate fundamental research on IR$^2$ SemCom, the performance limits of a text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI.
Adversarial Learning for Implicit Semantic-Aware Communications
In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user.
Enabling the Wireless Metaverse via Semantic Multiverse Communication
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements.
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study.
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning
The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task.
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.
Semantic-Native Communication: A Simplicial Complex Perspective
In this paper, we study semantic communication from a topological space perspective, in which higher-order data semantics live in a simplicial complex.
Differentially Private CutMix for Split Learning with Vision Transformer
Split learning (SL) detours this by communicating smashed data at a cut-layer, yet suffers from data privacy leakage and large communication costs caused by high similarity between ViT' s smashed data and input data.
Federated and Meta learning over Non-Wireless and Wireless Networks: A Tutorial
Typically, FL focuses on learning a global model for a given task and all devices and hence cannot adapt the model to devices with different data distributions.
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system.
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift.
Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities
The sixth generation (6G) wireless networks aim to achieve ultra-high data transmission rates, very low latency and enhanced energy-efficiency.
Slimmable Quantum Federated Learning
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL).
Towards Semantic Communication Protocols: A Probabilistic Logic Perspective
Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications.
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Leveraging this, we develop a novel SL framework for ViT, coined CutMixSL, communicating CutSmashed data.
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks
In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs).
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resource demands, while violating privacy.
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Newton-type methods are popular in federated learning due to their fast convergence.
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach
In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs.
Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach
Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users.
Learning Generalized Wireless MAC Communication Protocols via Abstraction
To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G wireless networks, conventional medium access control (MAC) procedures need to evolve to enable base stations (BSs) and user equipments (UEs) to automatically learn innovative MAC protocols catering to extremely diverse services.
Xavier-Enabled Extreme Reservoir Machine for Millimeter-Wave Beamspace Channel Tracking
In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism.
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces
The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives.
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence.
Time-triggered Federated Learning over Wireless Networks
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner.
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.
Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks
In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency.
THz-Empowered UAVs in 6G: Opportunities, Challenges, and Trade-Offs
Envisioned use cases of unmanned aerial vehicles (UAVs) impose new service requirements in terms of data rate, latency, and sensing accuracy, to name a few.
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.
Attention Based Communication and Control for Multi-UAV Path Planning
Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning.
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
In recent years, there have been great advances in the field of decentralized learning with private data.
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.
Learning Emergent Random Access Protocol for LEO Satellite Networks
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems.
Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines.
Federated Distributionally Robust Optimization for Phase Configuration of RISs
In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting.
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation.
Semantics-Native Communication with Contextual Reasoning
Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks.
Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning
In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources.
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels
In this paper, we propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.
Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation
Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power.
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent
In this paper, we propose an energy-efficient federated meta-learning framework.
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.
Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations
In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated.
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples.
Communication-Efficient and Personalized Federated Lottery Ticket Learning
The lottery ticket hypothesis (LTH) claims that a deep neural network (i. e., ground network) contains a number of subnetworks (i. e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network.
Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction
Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.
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.
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning
The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users.
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%.
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning
Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers.
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.
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus
Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data.
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems
Next-generation autonomous and networked industrial systems (i. e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing.
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
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.
Cross Layer Optimization and Distributed Reinforcement Learning Approach for Tile-Based 360 Degree Wireless Video Streaming
Wirelessly streaming high quality 360 degree videos is still a challenging problem.
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner.
Federated Knowledge Distillation
The goal of this chapter is to provide a deep understanding of FD while demonstrating its communication efficiency and applicability to a variety of tasks.
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
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks
Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62. 56x higher peak throughput and 21. 09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5G NTNs.
When Wireless Communications Meet Computer Vision in Beyond 5G
This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications).
Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces
Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable attention as a low-cost, hardware-efficient, and highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation.
Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL.
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems
Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics.
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth.
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).
Federated Learning in Vehicular Networks
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response.
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent.
6G White Paper on Edge Intelligence
We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers.
Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation
Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energy efficiency.
Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning
To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets.
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory
Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions.
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.
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.
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation
In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI).
Networking and Internet Architecture
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases.
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.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
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.
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM).
A Crowdsourcing Framework for On-Device Federated Learning
Federated learning (FL) rests on the notion of training a global model in a decentralized manner.
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).
Remote UAV Online Path Planning via Neural Network Based Opportunistic Control
This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB.
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).
Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.
Federated Reinforcement Distillation with Proxy Experience Memory
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models.
Multi-hop Federated Private Data Augmentation with Sample Compression
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity.
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications
To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs.
Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach
Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations.
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks
In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated.
Wireless Network Intelligence at the Edge
), requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML).
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples.
Blockchained On-Device Federated Learning
By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified.
Information Theory Networking and Internet Architecture 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
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services.
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.
Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach
The use of small cell deployments in heterogeneous network (HetNet) environments is expected to be a key feature of 4G networks and beyond, and essential for providing higher user throughput and cell-edge coverage.
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.
