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Found 24 papers, 2 papers with code
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
In this paper, we formulate a stochastic optimization problem to optimize the VFL training performance, considering the energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic scheduling (VEDS) algorithm to solve it.
Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems
In this article, a task-oriented wireless communication framework is proposed to jointly optimize the communication scheme and the CP procedure.
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective
Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages.
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner.
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner.
MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving
Specifically, we design a mobility-aware sensor scheduling (MASS) algorithm based on the restless multi-armed bandit (RMAB) theory to maximize the expected average perception gain.
SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
In particular, parallel computing resources on the platforms, such as graphics processing units (GPUs), have higher computational and energy efficiency with larger batch sizes.
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.
MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks
Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs.
DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
In this work, we release DOLPHINS: Dataset for cOllaborative Perception enabled Harmonious and INterconnected Self-driving, as a new simulated large-scale various-scenario multi-view multi-modality autonomous driving dataset, which provides a ground-breaking benchmark platform for interconnected autonomous driving.
Multi-user Co-inference with Batch Processing Capable Edge Server
To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint.
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning
In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation.
Online V2X Scheduling for Raw-Level Cooperative Perception
Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence.
Coded Computation across Shared Heterogeneous Workers with Communication Delay
In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation.
Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance under energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are included.
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy.
Device Scheduling with Fast Convergence for Wireless Federated Learning
In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels.
Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning
Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth.
Distributed Policy Learning Based Random Access for Diversified QoS Requirements
Future wireless access networks need to support diversified quality of service (QoS) metrics required by various types of Internet-of-Things (IoT) devices, e. g., age of information (AoI) for status generating sources and ultra low latency for safety information in vehicular networks.
A Two-Step Learning and Interpolation Method for Location-Based Channel Database
Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers.
Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks
In this paper, we propose a learning-based low-overhead beam alignment method for vehicle-to-infrastructure communication in vehicular networks.
Inferring Remote Channel State Information: Cramér-Rao Lower Bound and Deep Learning Implementation
Channel state information (CSI) is of vital importance in wireless communication systems.
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach
Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions.
A Block Regression Model for Short-Term Mobile Traffic Forecasting
Based on these characteristics, we propose a \emph{Block Regression} ({BR}) model for mobile traffic forecasting.
