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Found 31 papers, 1 papers with code
A Reconfigurable Subarray Architecture and Hybrid Beamforming for Millimeter-Wave Dual-Function-Radar-Communication Systems
Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks.
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability.
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server.
OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface
Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence.
Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach
In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links.
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Towards personalized generative services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources.
Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs.
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems.
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models.
Semantics-Empowered Communication: A Tutorial-cum-Survey
Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e. g., theories, applications, metrics and implementations) in both academia and industry.
Intelligent Computing: The Latest Advances, Challenges and Future
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.
Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications
It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11x11 is deployed.
DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning
The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder.
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems
We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission.
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty
The stability and the robustness of deep MARL to practical challenges is still an open research problem.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Self-Organizing mmWave MIMO Cell-Free Networks With Hybrid Beamforming: A Hierarchical DRL-Based Design
Furthermore, to mitigate interference between different cell-free subnetworks, we develop a novel hybrid analog beamsteering-digital beamforming model that zero-forces interference among cell-free subnetworks and at the same time maximizes the instantaneous sum-rate of all UEs within each subnetwork.
Ambient Backscatter-Assisted Wireless-Powered Relaying
With different mode selection protocols, we characterize the success probability and ergodic capacity of a dual-hop relaying system with the hybrid relay in the field of randomly located ambient transmitters.
Information Theory Networking and Internet Architecture Information Theory
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks
Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices.
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial
In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled 6G networks.
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
Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line and Off-policy Bandit Solutions
Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
First, we propose a fully centralized beamforming method that uses the deep deterministic policy gradient algorithm (DDPG) with continuous space.
Optimization of Wireless Relaying With Flexible UAV-Borne Reflecting Surfaces
This paper presents a theoretical framework to analyze the performance of integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes.
Multiple Access in Dynamic Cell-Free Networks: Outage Performance and Deep Reinforcement Learning-Based Design
To significantly reduce the complexity of joint processing of users' signals in presence of a large number of devices and APs, we propose a novel dynamic cell-free network architecture.
Data-Driven Machine Learning Techniques for Self-healing in Cellular Wireless Networks: Challenges and Solutions
As an important component in SON, self-healing is defined as a network paradigm where the faults of target networks are mitigated or recovered by automatically triggering a series of actions such as detection, diagnosis and compensation.
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks
For benchmarking the proposed approach, we use a Genetic Algorithm (GA) to obtain near-optimal power allocation solution.
Machine Learning in IoT Security: Current Solutions and Future Challenges
We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks.
Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
In this article, we study the distributed cell association problem for energy harvesting IoT devices in UD-SCNs.
Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model
We consider a distributed downlink user association problem in a small cell network, where small cells obtain the required energy for providing wireless services to users through ambient energy harvesting.
Multi-armed Bandits with Application to 5G Small Cells
This requires the next generation wireless networks to move towards new networking paradigms that are able to efficiently support resource-demanding applications such as personalized mobile services.
