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Found 69 papers, 10 papers with code
Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse
Federated learning (FL) has emerged as a promising technique for collaboratively training AI models while preserving data privacy.
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.
FedsLLM: Federated Split Learning for Large Language Models over Communication Networks
This paper models the minimization of the training delay by integrating computation and communication optimization, simplifying the optimization problem into a convex problem to find the optimal solution.
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.
Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning
The near-field beam training in ELAA requires both angle and distance information, which inevitably leads to a significant increase in the beam training overhead.
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.
Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate
How to reduce the pilot overhead required for channel estimation?
Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets
A key revelation of our research is the feasibility of sampling training data for large traffic systems from simulations conducted on smaller traffic systems.
Digital versus Analog Transmissions for Federated Learning over Wireless Networks
In this paper, we quantitatively compare these two effective communication schemes, i. e., digital and analog ones, for wireless federated learning (FL) over resource-constrained networks, highlighting their essential differences as well as their respective application scenarios.
DMCE: Diffusion Model Channel Enhancer for Multi-User Semantic Communication Systems
To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features.
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.
Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer
For such a channel mapping task, inspired by the intrinsic coupling across the space and frequency domains, this letter proposes to use interleaved learning with partial antenna and subcarrier characteristics to represent the whole MIMO-OFDM channel.
Robust Target Detection of Intelligent Integrated Optical Camera and mmWave Radar System
To improve the target detection performance under complex real-world scenarios, this paper proposes an intelligent integrated optical camera and millimeter-wave (mmWave) radar system.
Adaptive Resource Allocation for Semantic Communication Networks
In order to investigate the performance of semantic communication networks, the quality of service for semantic communication (SC-QoS), including the semantic quantization efficiency (SQE) and transmission latency, is proposed for the first time.
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.
Joint Communication and Computation Framework for Goal-Oriented Semantic Communication with Distortion Rate Resilience
Recent research efforts on semantic communication have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems.
Online Resource Allocation for Semantic-Aware Edge Computing Systems
To solve this problem, an online low-complexity algorithm is proposed.
Resource Allocation for Semantic-Aware Mobile Edge Computing Systems
Moreover, the closed-form optimal solution of the semantic extraction factor is derived.
Semantic Information Extraction for Text Data with Probability Graph
In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied.
Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses
In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool.
MIMO Precoding Design with QoS and Per-Antenna Power Constraints
Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem.
Distributed Learning over Networks with Graph-Attention-Based Personalization
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model.
Semantic-aware Digital Twin for Metaverse: A Comprehensive Review
To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic awareness has been proposed as a means for enabling accurate and task-oriented information extraction with inherent intelligence.
Self-information Domain-based Neural CSI Compression with Feature Coupling
The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression.
From Data-driven Learning to Physics-inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE
Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving.
Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning
Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G.
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics
In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications.
TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection
The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s.
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation
The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit.
Over-the-Air Split Machine Learning in Wireless MIMO Networks
In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.
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.
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.
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences.
Mobile MIMO Channel Prediction with ODE-RNN: a Physics-Inspired Adaptive Approach
Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead.
Environment Sensing Considering the Occlusion Effect: A Multi-View Approach
In this paper, we consider the problem of sensing the environment within a wireless cellular framework.
Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface under Imperfect CSI
This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks.
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks
In this paper, a joint model split and neural architecture search (JMSNAS) framework is proposed to automatically generate and deploy a DNN model over a mobile edge network.
Neural Architecture Dilation for Adversarial Robustness
With the tremendous advances in the architecture and scale of convolutional neural networks (CNNs) over the past few decades, they can easily reach or even exceed the performance of humans in certain tasks.
Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks
In this paper, a novel framework for proactive caching is proposed.
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.
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.
Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.
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
A Survey on Visual Transformer
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.
Adapting Neural Architectures Between Domains
The power of deep neural networks is to be unleashed for analyzing a large volume of data (e. g. ImageNet), but the architecture search is often executed on another smaller dataset (e. g. CIFAR-10) to finish it in a feasible time.
Federated Learning in Multi-RIS Aided Systems
This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs).
Information Theory Signal Processing Information Theory
Beamforming Design for Multiuser Transmission Through Reconfigurable Intelligent Surface
To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem.
Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method
Wireless communication in the TeraHertz band (0. 1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems.
Searching for Low-Bit Weights in Quantized Neural Networks
Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators.
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.
Cooperative Rate-Splitting for Secrecy Sum-Rate Enhancement in Multi-antenna Broadcast Channels
In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes.
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.
Reflections in the Sky: Joint Trajectory and Passive Beamforming Design for Secure UAV Networks with Reconfigurable Intelligent Surface
This paper investigates the problem of secure energy efficiency maximization for a reconfigurable intelligent surface (RIS) assisted uplink wireless communication system, where an unmanned aerial vehicle (UAV) equipped with an RIS works as a mobile relay between the base station (BS) and a group of users.
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.
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.
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.
Discernible Image Compression
Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
This letter investigates a channel assignment problem in uplink wireless communication systems.
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images
Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.
Ranked #29 on
Weakly Supervised Object Detection
on PASCAL VOC 2012 test
(using extra training data)
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.
CARS: Continuous Evolution for Efficient Neural Architecture Search
Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.
Data-Free Learning of Student Networks
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
Revisiting Perspective Information for Efficient Crowd Counting
Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions.
