17 citations · 41 across the 13 of their papers we have counts for
13 papers
Learning to Localize with Attention: from sparse mmWave channel estimates from a single BS to high accuracy 3D location
Yun Chen, Nuria González-Prelcic, Takayuki Shimizu +1
One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the…
Deep Masked Graph Matching for Correspondence Identification in Collaborative Perception
Peng Gao, Qingzhao Zhu, Hongsheng Lu +2
Correspondence identification (CoID) is an essential component for collaborative perception in multi-robot systems, such as connected autonomous vehicles. The goal of CoID is to id…
ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences
Hansi Liu, Kristin Dana, Marco Gruteser +1
In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to…
Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach
Yun Chen, Joan Palacios, Nuria González-Prelcic +2
High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can,…
Adaptive Neural Network-based OFDM Receivers
Moritz Benedikt Fischer, Sebastian Dörner, Sebastian Cammerer +3
We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel cond…
The Transdimensional Poisson Process for Vehicular Network Analysis
Jeya Pradha Jeyaraj, Martin Haenggi, Ahmed Hamdi Sakr +1
A comprehensive vehicular network analysis requires modeling the street system and vehicle locations. Even when Poisson point processes (PPPs) are used to model the vehicle locatio…
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation
Peng Gao, Brian Reily, Rui Guo +3
Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with r…
Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications
Moritz Benedikt Fischer, Sebastian Dörner, Sebastian Cammerer +4
We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. F…
Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors
Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti +3
The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication net…
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
Peng Gao, Rui Guo, Hongsheng Lu +1
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent sy…
Securing Vehicle-to-Everything (V2X) Communication Platforms
Monowar Hasan, Sibin Mohan, Takayuki Shimizu +1
Modern vehicular wireless technology enables vehicles to exchange information at any time, from any place, to any network -- forms the vehicle-to-everything (V2X) communication pla…
Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks
Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti +3
The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally…
Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applications
Yicong Wang, Gustavo de Veciana, Takayuki Shimizu +1
A critical requirement for automated driving systems is enabling situational awareness in dynamically changing environments. To that end vehicles will be equipped with diverse sens…