University of Oulu

Khan, H., Majid Butt, M., Samarakoon, S., Sehier, P. & Bennis, M. (2020). Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation. In 2020 IEEE International Conference on Communications Workshops (ICC), Dublin, Ireland, 7-11 June 2020: Proceedings, 9145297. doi: 10.1109/ICCWorkshops49005.2020.9145297

Deep learning assisted CSI estimation for joint URLLC and eMBB resource allocation

Saved in:
Author: Khan, Hamza1; Majid Butt, M.2; Samarakoon, Sumudu1;
Organizations: 1Centre for Wireless Communication, University of Oulu, Finland
2Nokia Bell Labs, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-10-13


Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energy efficiency. Reaping the benefits of MIMO transmission can be fully harnessed if the channel state information (CSI) is available at the transmitter side. However, the acquisition of transmitter side CSI entails many challenges. In this paper, we propose a deep learning assisted CSI estimation technique in highly mobile vehicular networks, based on the fact that the propagation environment (scatterers, reflectors) is almost identical thereby allowing a data driven deep neural network (DNN) to learn the non-linear CSI relations with negligible overhead. Moreover, we formulate and solve a dynamic network slicing based resource allocation problem for vehicular user equipments (VUEs) requesting enhanced mobile broadband (eMBB) and ultra-reliable low latency (URLLC) traffic slices. The formulation considers a threshold rate violation probability minimization for the eMBB slice while satisfying a probabilistic threshold rate criterion for the URLLC slice. Simulation result shows that an overhead reduction of 50% can be achieved with 12% increase in threshold violations compared to an ideal case with perfect CSI knowledge.

see all

Series: IEEE/CIC International Conference on Communications in China - Workshops
ISSN: 2474-9133
ISSN-E: 2474-9141
ISSN-L: 2474-9133
Article number: 9145297
DOI: 10.1109/ICCWorkshops49005.2020.9145297
Host publication: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 7–11 June 2020 : proceedings
Conference: IEEE International Conference on Communications Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by the projects SAFARI, High5, and 6Genesis Flagship. First author is grateful for the grants received from HPY, Nokia, and Walter Ahlstrmin Foundation.
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.