Li, TH;
Khandaker, MRA;
Tariq, F;
Wong, KK;
Khan, RT;
(2019)
Learning the wireless V2I channels using deep neural networks.
In:
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).
IEEE: Honolulu, HI, USA.
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Abstract
For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to- infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning the wireless V2I channels using deep neural networks |
| Event: | VTC2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/VTCFall.2019.8891562 |
| Publisher version: | https://doi.org/10.1109/VTCFall.2019.8891562 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Channel estimation , Wireless communication , Machine learning , OFDM , Biological neural networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10087717 |
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