Zhenyu Liu

Orcid: 0000-0003-0251-3267

Affiliations:
  • Beijing University of Posts and Telecommunications, MOE, Key Laboratory of Universal Wireless Communications, China


According to our database1, Zhenyu Liu authored at least 25 papers between 2015 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework.
CoRR, 2023

Training CSI Feedback Model with Federated Learning in Massive Mimo Systems.
Proceedings of the 8th IEEE International Conference on Network Intelligence and Digital Content, 2023

2022
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback.
IEEE Trans. Wirel. Commun., 2022

Training Deep Learning Models for Massive MIMO CSI Feedback with Small Datasets in New Environments.
CoRR, 2022

Lifetime Maximization for UAV-Enabled Integrated Localization and Communication Networks in Emergency Scenarios.
Proceedings of the 14th International Conference on Wireless Communications and Signal Processing, 2022

An Efficient and Robust UAVs' Path Planning Approach for Timely Data Collection in Wireless Sensor Networks.
Proceedings of the IEEE Wireless Communications and Networking Conference, 2022

Propagation Path Loss Models in Forest Scenario at 605 MHz.
Proceedings of the 96th Vehicular Technology Conference, 2022

Clustering-Enabled Prioritized Access Control for Massive Machine-Type Communications in Smart Grid.
Proceedings of the 96th Vehicular Technology Conference, 2022

2021
Deep Learning Phase Compression for MIMO CSI Feedback by Exploiting FDD Channel Reciprocity.
IEEE Wirel. Commun. Lett., 2021

Joint Optimization of UAVs 3-D Placement and Power Allocation in Emergency Communications.
Proceedings of the IEEE Global Communications Conference, 2021

2020
Overcoming the Channel Estimation Barrier in Massive MIMO Communication via Deep Learning.
IEEE Wirel. Commun., 2020

MARP: A Distributed MAC Layer Attack Resistant Pseudonym Scheme for VANET.
IEEE Trans. Dependable Secur. Comput., 2020

An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting.
IEEE Trans. Commun., 2020

Wireless Channel Data Augmentation for Artificial Intelligence of Things in Industrial Environment Using Generative Adversarial Networks.
Proceedings of the 18th IEEE International Conference on Industrial Informatics, 2020

Spherical Normalization for Learned Compressive Feedback in Massive MIMO CSI Acquisition.
Proceedings of the 2020 IEEE International Conference on Communications Workshops, 2020

2019
Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback.
IEEE Wirel. Commun. Lett., 2019

Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems.
CoRR, 2019

2017
Modeling and analysis of indoor coverage probability for future 3D dense mobile networks.
Proceedings of the 20th International Symposium on Wireless Personal Multimedia Communications, 2017

BLE-horn: A smartphone-based bluetooth low energy vehicle-to-pedestrian safety system.
Proceedings of the 9th International Conference on Wireless Communications and Signal Processing, 2017

Energy efficiency analysis for future 3D ultra dense mobile networks with sleep mode.
Proceedings of the 28th IEEE Annual International Symposium on Personal, 2017

Security/Reliability-Aware Relay Selection with Connection Duration Constraints for Vehicular Networks.
Proceedings of the Algorithms and Architectures for Parallel Processing, 2017

2016
SenSafe: A Smartphone-Based Traffic Safety Framework by Sensing Vehicle and Pedestrian Behaviors.
Mob. Inf. Syst., 2016

Implementation and performance measurement of a V2X communication system for vehicle and pedestrian safety.
Int. J. Distributed Sens. Networks, 2016

2015
Implementing on-board diagnostic and GPS on VANET to safe the vehicle.
Proceedings of the International Conference on Connected Vehicles and Expo, 2015

POFS: A novel pedestrian-oriented forewarning system for vulnerable pedestrian safety.
Proceedings of the International Conference on Connected Vehicles and Expo, 2015


  Loading...