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Found 54 papers, 4 papers with code
Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning
To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed.
Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning
To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss.
Joint Vehicle Connection and Beamforming Optimization in Digital Twin Assisted Integrated Sensing and Communication Vehicular Networks
To solve the simplified problem, this paper introduces both greedy and heuristic algorithms through optimizing both vehicle assignments and predictive beamforming.
Joint Beamforming and Antenna Design for Near-Field Fluid Antenna System
In this letter, we study the energy efficiency maximization problem for a fluid antenna system (FAS) in near field communications.
Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks
To bridge this gap, we introduce a novel digital twin-assisted optimization framework, called D-REC, which integrates reinforcement learning (RL) with diverse intervention modules to ensure reliable caching in nextG wireless networks.
Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
In recent years, the complexity of 5G and beyond wireless networks has escalated, prompting a need for innovative frameworks to facilitate flexible management and efficient deployment.
Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges
The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives.
A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges
This paper provides a comprehensive survey of VLP systems.
Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking
Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power.
Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks
By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained.
A Joint Gradient and Loss Based Clustered Federated Learning Design
In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training.
Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions
Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions.
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices.
Optimization of Image Transmission in a Cooperative Semantic Communication Networks
In this paper, a semantic communication framework for image transmission is developed.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+1
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.
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage.
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.
Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks
Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS.
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.
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.
Neural Architecture Searching for Facial Attributes-based Depression Recognition
In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly implemented in a small dataset.
Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated.
MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
MBDP consists of two kinds of dropout mechanisms, where the rollout-dropout aims to improve the robustness with a small cost of sample efficiency, while the model-dropout is designed to compensate for the lost efficiency at a slight expense of robustness.
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.
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance.
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications.
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.
A Comprehensive Survey on 6G Networks:Applications, Core Services, Enabling Technologies, and Future Challenges
Cellular Internet of Things (IoT) is considered as de facto paradigm to improve the communication and computation systems.
Networking and Internet Architecture
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.
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
To this end, the fundamentals of this framework are first introduced.
Optimal Resource Allocation for Multi-UAV Assisted Visible Light Communication
To meet the traffic and illumination demands of the ground users while minimizing the energy consumption of the UAVs, one must optimize UAV deployment, phase shift of RISs, user association and RIS association.
Information Theory Information Theory
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.
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks.
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks
Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users.
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.
UVeQFed: Universal Vector Quantization for Federated Learning
We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
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.
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.
Machine Learning for Predictive Deployment of UAVs with Multiple Access
In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied.
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.
Artificial Intelligence Aided Next-Generation Networks Relying on UAVs
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.
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.
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.
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.
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.
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
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.
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.
