Search Results for author:
Found 38 papers, 7 papers with code
SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera
Since the spike camera relies on temporal integration instead of temporal differentiation used by event cameras, our proposed TfS loss maintains manageable training costs.
Decision Transformer for Wireless Communications: A New Paradigm of Resource Management
By leveraging the power of DT models learned over extensive datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in a new context, e. g., similar tasks with different state and action spaces, compared with DRL.
Semi-Passive Intelligent Reflecting Surface Enabled Sensing Systems
Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS).
Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
The resulting large-scale complicated network optimization problems are beyond the capability of model-based theoretical methods due to the overwhelming computational complexity and the long processing time.
UAV-enabled Integrated Sensing and Communication: Tracking Design and Optimization
Specifically, a weighted sum of predicted posterior Cram\'er-Rao bound (PCRB) for object relative position and velocity estimation is minimized by optimizing the UAV trajectory, where an efficient solution is obtained based on the successive convex approximation method.
Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning
we formulate a joint device scheduling, and power allocation problem to maximize the number of scheduled devices.
Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server.
Intelligent Surface Empowered Integrated Sensing and Communication: From Coexistence to Reciprocity
Integrated sensing and communication (ISAC) has attracted growing interests for sixth-generation (6G) and beyond wireless networks.
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
However, the domain discrepancy and data scarcity problems among clients deteriorate the performance of the global FL model.
Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT.
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training.
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users.
Intelligent Omni Surfaces assisted Integrated Multi Target Sensing and Multi User MIMO Communications
Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC).
A Laplacian Pyramid Based Generative H&E Stain Augmentation Network
Hematoxylin and Eosin (H&E) staining is a widely used sample preparation procedure for enhancing the saturation of tissue sections and the contrast between nuclei and cytoplasm in histology images for medical diagnostics.
Transmissive Reconfigurable Intelligent Surface Transmitter Empowered Cognitive RSMA Networks
In this paper, we investigated the downlink transmission problem of a cognitive radio network (CRN) equipped with a novel transmissive reconfigurable intelligent surface (TRIS) transmitter.
Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning
Network energy efficiency is a main pillar in the design and operation of wireless communication systems.
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training.
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local training, thereby mitigating the performance degradation induced by DP and and reducing the number of transmission parameters over wireless channels.
Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs
In our experiment, we demonstrate that our proposed method outperforms existing image-to-image translation methods for stain translation to multiple IHC stains.
Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks
Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly.
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks.
Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks
We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources.
Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless Communications
This manuscript presents an energy-efficient alternating optimization framework based on intelligent reflective surfaces (IRS) aided non-orthogonal multiple access beamforming (NOMA-BF) system for 6G wireless communications.
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Deterministic Point Cloud Registration via Novel Transformation Decomposition
Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF).
Multi-Antenna Systems by Transmissive Reconfigurable Meta-Surface
Reconfigurable meta-surface (RMS) is proposed as a very promising and novel technology, which is composed of a large number of low-cost passive elements, and can achieve passive beamforming by controlling the amplitude and phase of incident electromagnetic (EM) waves.
Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors
In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint.
Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks
Aiming at the limited battery capacity of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the wireless energy transmission efficiency and coverage area of the UAV SWIPT networks.
Hybrid Supervision Learning for Pathology Whole Slide Image Classification
To handle this problem, we propose a hybrid supervision learning framework for this kind of high resolution images with sufficient image-level coarse annotations and a few pixel-level fine labels.
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA
With the explosively increasing demands on the network capacity, throughput and number of connected wireless devices, massive connectivity is an urgent problem for the next generation wireless communications.
Energy-Efficient NOMA Multicasting System for 5G Cellular V2X Communications with Imperfect CSI
The proposed system multicast the information through low complexity optimal power allocation algorithms used under channel outage probability constraint of vehicles with imperfect CSI, QoS constraints of vehicles, and transmit power limits constraint of RSUs.
Deep neural network for optimal retirement consumption in defined contribution pension system
In this paper, we develop a deep neural network approach to solve a lifetime expected mortality-weighted utility-based model for optimal consumption in the decumulation phase of a defined contribution pension system.
Neutral bots probe political bias on social media
Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.
Social and Information Networks Computers and Society
SCMA Spectral and Energy Efficiency with QoS
Moreover, the proposed method is compared with orthogonal frequency devision multiple access (OFDMA) and code devision multiple access (CDMA) in terms of spectral efficiency (SE) and energy efficiency (EE) respectively.
Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
The tool currently supports both metal layer and via layer generation.
Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death.
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.
