S. Chen, K. Vu, S. Zhou, Z. Niu, M. Bennis and M. Latva-Aho, "1 A Deep Reinforcement Learning Framework to Combat Dynamic Blockage in mmWave V2X Networks," 2020 2nd 6G Wireless Summit (6G SUMMIT), Levi, Finland, 2020, pp. 1-5, doi: 10.1109/6GSUMMIT49458.2020.9083744
A deep reinforcement learning framework to combat dynamic blockage in mmWave V2X networks
|Author:||Chen, Sheng1; Vu, Kien2,3; Zhou, Sheng1;|
1Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
3Nokia Networks, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020100176326
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-10-01
Millimeter Wave (mmWave) systems are considered as one of the key technologies in future wireless systems due to the abundant spectrum resources in mmWave band. With the aim of achieving the capacity requirements in vehicular networks, large antenna arrays can be deployed at both the road side units (RSUs) side and the vehicles side. However, dynamic blockage caused by mobile obstacles in mmWave bands may hinder the system reliability. In this work, we study the temporal effects of dynamic blockage in vehicular networks and propose a deep reinforcement learning framework to overcome dynamic blockage. By dynamically adjusting blockage detection parameters and making intelligent handover decisions according to the observed states, system reliability can be significantly improved. Simulation results based on ray-tracing channel data show that the proposed scheme reduces the violation probability by 28.9% over conventional schemes.
|Pages:||1 - 5|
2020 2nd 6G Wireless Summit (6G SUMMIT)
6G Wireless Summit
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is sponsored in part by the Nature Science Foundation of China (No. 61871254, No. 91638204, No. 61861136003), National Key R&D Program of China 2018YFB0105005 and 2018YFB1800800, the Academy of Finland 6Genesis Flagship project under grant 318927 and Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles. The Nokia Foundation, the UniOGS travel grant and the NVIDIA Corporation are also acknowledged.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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