TY - GEN
T1 - Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11p Standard
AU - Gizzini, Abdul Karim
AU - Chafii, Marwa
AU - Ehsanfar, Shahab
AU - Shubair, Raed M.
N1 - Funding Information:
Authors acknowledge the CY Initiative of Excellence for the support of the project through the ASIA Chair of Excellence Grant (PIA/ANR-16-IDEX-0008).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In vehicular communications, reliable channel estimation is critical for the system performance due to the doubly-dispersive nature of vehicular channels. IEEE 802.11p standard allocates insufficient pilots for accurate channel tracking. Consequently, conventional IEEE 802.11p estimators suffer from a considerable performance degradation, especially in high mobility scenarios. Recently, deep learning (DL) techniques have been employed for IEEE 802.11p channel estimation. Neverthe-less, these methods suffer either from performance degradation in very high mobility scenarios or from large computational complexity. In this paper, these limitations are solved using a long short term memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to estimate the channel, followed by temporal averaging (TA) processing as a noise alleviation technique. Moreover, the noise mitigation ratio is determined analytically, thus validating the TA processing ability in improving the overall performance. Simulation results reveal the performance superiority of the proposed schemes compared to the recently proposed DL-based estimators, while recording a significant reduction in the computational complexity.
AB - In vehicular communications, reliable channel estimation is critical for the system performance due to the doubly-dispersive nature of vehicular channels. IEEE 802.11p standard allocates insufficient pilots for accurate channel tracking. Consequently, conventional IEEE 802.11p estimators suffer from a considerable performance degradation, especially in high mobility scenarios. Recently, deep learning (DL) techniques have been employed for IEEE 802.11p channel estimation. Neverthe-less, these methods suffer either from performance degradation in very high mobility scenarios or from large computational complexity. In this paper, these limitations are solved using a long short term memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to estimate the channel, followed by temporal averaging (TA) processing as a noise alleviation technique. Moreover, the noise mitigation ratio is determined analytically, thus validating the TA processing ability in improving the overall performance. Simulation results reveal the performance superiority of the proposed schemes compared to the recently proposed DL-based estimators, while recording a significant reduction in the computational complexity.
KW - Channel estimation
KW - IEEE 802.11p standard
KW - LSTM
KW - deep learning
KW - ve-hicular communications
UR - http://www.scopus.com/inward/record.url?scp=85127250726&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127250726&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM46510.2021.9685409
DO - 10.1109/GLOBECOM46510.2021.9685409
M3 - Conference contribution
AN - SCOPUS:85127250726
T3 - 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
BT - 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021
Y2 - 7 December 2021 through 11 December 2021
ER -