Abstract
IEEE 802.11p standard is specially developed to define vehicular communications requirements and support cooperative intelligent transport systems. In such environment, reliable channel estimation is considered as a major critical challenge for ensuring the system performance due to the extremely time-varying characteristic of vehicular channels. The channel estimation of IEEE 802.11p is preamble based, which becomes inaccurate in high mobility scenarios. The major challenge is to track the channel variations over the course of packet length while adhering to the standard specifications. The motivation behind this paper is to overcome this issue by proposing a novel deep learning based channel estimation scheme for IEEE 802.11p that optimizes the use of deep neural networks (DNN) to accurately learn the statistics of the spectral temporal averaging (STA) channel estimates and to track their changes over time. Simulation results demonstrate that the proposed channel estimation scheme STA-DNN significantly outperforms classical channel estimators in terms of bit error rate. The proposed STA-DNN architectures also achieve better estimation performance than the recently proposed auto-encoder DNN based channel estimation with at least 55.74% of computational complexity decrease.
Highlights
The cooperative intelligent transportation system (C-ITS) has been developed to provide various traffic services such as road safety, route planning and congestion avoidance
In order to have a good approximation of the unreliable subcarriers set size, we ran our simulation 10000 times, and we found that an average of Kint = 10 subcarriers are considered as unreliable subcarriers in each received orthogonal frequencydivision multiplexing (OFDM) symbol
We have studied the IEEE 802.11p specifications, and we have proposed a deep learning based scheme that adheres to the standard structure
Summary
The cooperative intelligent transportation system (C-ITS) has been developed to provide various traffic services such as road safety, route planning and congestion avoidance. The authors in [3] have proposed constructed data pilots (CDP) scheme to improve the accuracy of the channel estimates by exploiting the correlation characteristics between each two adjacent symbols through utilizing data subcarriers as pilots, such that the data subcarriers from previous OFDM symbol are used as preamble to estimate the channel for the current symbol. This process is influenced by the demapping error, which depends on the accuracy of previous estimation and the noise level This error propagates and increases from one symbol to another all over the frame causing a considerable reliability degradation in realistic vehicular environments. In order to improve the aforementioned schemes in terms of higher accuracy and lower complexity, we propose in this work a hybrid approach In this approach, an initial coarse estimation is obtained using STA, and a fine estimation is achieved by means of DNN.
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