TY - GEN
T1 - Learning the wireless V2I channels using deep neural networks
AU - Li, Tian Hao
AU - Khandaker, Muhammad R. A.
AU - Tariq, Faisal
AU - Wong, Kai-Kit
AU - Khan, Risala T.
PY - 2019/11/7
Y1 - 2019/11/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075249999&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2019.8891562
DO - 10.1109/VTCFall.2019.8891562
M3 - Conference contribution
AN - SCOPUS:85075249999
T3 - IEEE Vehicular Technology Conference
BT - 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
PB - IEEE
T2 - 90th IEEE Vehicular Technology Conference 2019
Y2 - 22 September 2019 through 25 September 2019
ER -