TY - GEN
T1 - Channel Estimation using Temporal Convolutional Networks for V2X Communications
AU - Jovane, Juan D.
AU - Lee, Chia Han
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To achieve satisfactory performance in vehicle-to-everything (V2X) communications, it is paramount to accurately estimate the channel. The traditional data-pilot aided (DPA) scheme and the variation of DPA, e.g., spectral temporal averaging (STA), have been adopted for IEEE 802.11p due to their low complexity, but their performances are not satisfactory. The more recently proposed time domain reliable test frequency domain interpolation (TRFI) scheme only marginally improves the performance. Deep neural network (DNN)-based estimators, e.g., STA-DNN and TRFI-DNN, have substantially improved the channel estimation, and the long short-term memory (LSTM)-based estimators, such as LSTM-DPA-TA and LSTM-MLP-DPA, achieve the state-of-the-art performance. LSTM-based estimators, however, have high computational complexity. In this paper, we propose a novel channel estimator that leverages temporal convolutional networks (TCNs) combined with the DPA procedure to estimate and track channel variations. Simulations on realistic V2X scenarios show that the proposed TCN-DPA channel estimation scheme outperforms existing methods in almost all V2X scenarios. The proposed estimator has about one order of magnitude improvement in terms of bit error rate compared to LSTM-based estimators. By exploiting the parallelism inherent in the TCN architecture, the computational complexity of the proposed TCN-DPA estimator is 40% and 47% lower than LSTM-DPA-TA and LSTM-MLP-DPA, respectively. Moreover, the training time of TCN-DPA is only 52% and 42% of the time of LSTM-DPA-TA and LSTM-MLP-DPA, respectively.
AB - To achieve satisfactory performance in vehicle-to-everything (V2X) communications, it is paramount to accurately estimate the channel. The traditional data-pilot aided (DPA) scheme and the variation of DPA, e.g., spectral temporal averaging (STA), have been adopted for IEEE 802.11p due to their low complexity, but their performances are not satisfactory. The more recently proposed time domain reliable test frequency domain interpolation (TRFI) scheme only marginally improves the performance. Deep neural network (DNN)-based estimators, e.g., STA-DNN and TRFI-DNN, have substantially improved the channel estimation, and the long short-term memory (LSTM)-based estimators, such as LSTM-DPA-TA and LSTM-MLP-DPA, achieve the state-of-the-art performance. LSTM-based estimators, however, have high computational complexity. In this paper, we propose a novel channel estimator that leverages temporal convolutional networks (TCNs) combined with the DPA procedure to estimate and track channel variations. Simulations on realistic V2X scenarios show that the proposed TCN-DPA channel estimation scheme outperforms existing methods in almost all V2X scenarios. The proposed estimator has about one order of magnitude improvement in terms of bit error rate compared to LSTM-based estimators. By exploiting the parallelism inherent in the TCN architecture, the computational complexity of the proposed TCN-DPA estimator is 40% and 47% lower than LSTM-DPA-TA and LSTM-MLP-DPA, respectively. Moreover, the training time of TCN-DPA is only 52% and 42% of the time of LSTM-DPA-TA and LSTM-MLP-DPA, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85178255188&partnerID=8YFLogxK
U2 - 10.1109/ICC45041.2023.10279602
DO - 10.1109/ICC45041.2023.10279602
M3 - Conference contribution
AN - SCOPUS:85178255188
T3 - IEEE International Conference on Communications
SP - 565
EP - 570
BT - ICC 2023 - IEEE International Conference on Communications
A2 - Zorzi, Michele
A2 - Tao, Meixia
A2 - Saad, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications, ICC 2023
Y2 - 28 May 2023 through 1 June 2023
ER -