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Found 73 papers, 20 papers with code
Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach
In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs).
DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV
Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL.
IREE Oriented Active RIS-Assisted Green communication System with Outdated CSI
Given the non-convex nature of the problem, we propose an Alternating Optimization Successive Approximation (AOSO) algorithm.
Movable Antenna Enhanced Networked Full-Duplex Integrated Sensing and Communication System
These challenges can be overcome by networked FD ISAC framework.
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
The proposed approach leverages the semantic information to optimize the allocation of communication resources.
Joint Discrete Antenna Positioning and Beamforming Optimization in Movable Antenna Enabled Full-Duplex ISAC Networks
We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power.
Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation
In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization.
V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario
Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized.
Beamforming Design for Intelligent Reffecting Surface Aided Near-Field THz Communications
Then, we extend the piece-wise far-field model to the IRS, based on which, a double-layer delta-delay (DLDD) IRS beamforming scheme is proposed.
Two-Timescale Design for Movable Antennas Enabled-Multiuser MIMO Systems
Within this new framework, we analyze the ergodic sum rate and develop efficient MA position optimization algorithms for both maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming schemes.
Towards TMA-Based Transmissive RIS Transceiver Enabled Downlink Communication Networks: A Consensus-ADMM Approach
This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver.
Movable Antennas Enabled Wireless-Powered NOMA: Continuous and Discrete Positioning Designs
This paper investigates a movable antenna (MA)-enabled wireless-powered communication network (WPCN), where multiple wireless devices (WDs) first harvest energy from the downlink (DL) signal broadcast by a hybrid access point (HAP) and then transmit information in the uplink (UL) using non-orthogonal multiple access.
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
In this paper, we propose a novel personalized FL (PFL) approach, named Adversarial Federated Consensus Learning (AFedCL), for the challenge of data heterogeneity across different clients in SDC.
Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users.
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training.
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
We utilize the Lyapunov method to decouple the formulated problem into a series of one-slot optimization problems and develop a two-stage optimization algorithm to achieve the optimal transmission power control and IoT device scheduling strategies.
Transmissive RIS Enabled Transceiver Systems:Architecture, Design Issues and Opportunities
Moreover, a near-far field channel model appropriate for this architecture is proposed.
Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy
In contemporary data-driven environments, the generation and processing of multivariate time series data is an omnipresent challenge, often complicated by time delays between different time series.
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data.
Simple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao
In recommendation systems, the matching stage is becoming increasingly critical, serving as the upper limit for the entire recommendation process.
Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU).
Energy-Aware UAV-Enabled Target Tracking: Online Optimization with Location Constraints
Specifically, our approach designs the UAV trajectory by solving two subproblems: the candidate trajectory optimization problem and the energy-aware backup trajectory optimization problem.
Cramer-Rao Bound Minimization for Movable Antenna-Assisted Multiuser Integrated Sensing and Communications
This paper investigates a movable antenna (MA)-assisted multiuser integrated sensing and communication (ISAC) system, where the base station (BS) and communication users are all equipped with MA for improving both the sensing and communication performance.
Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles.
Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput.
Channel Characterization of IRS-assisted Resonant Beam Communication Systems
The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking.
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints.
Multi-Functional Beamforming Design for Integrated Sensing, Communication, and Computation
This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple singleantenna communication users.
Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks
Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments.
Movable Antenna Enhanced NOMA Short-Packet Transmission
This letter investigates a short-packet downlink transmission system using non-orthogonal multiple access (NOMA) enhanced via movable antenna (MA).
Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V.
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server.
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices.
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
To evaluate the learning performance of T-SFL, we provide an upper bound on the global loss function.
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation of GNNs.
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 Transformers for Wireless Communications: A New Paradigm of Resource Management
By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios 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.
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.
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.
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
+2
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.
