Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard

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. INDEX TERMS Channel estimation, deep learning, DNN, IEEE 802.11p standard, vehicular channels.

CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications

“…Each equalization step consists of K on complex valued division, therefore the equalization in (8), and (9) requires 16K on multiplications/divisions and 6K on summations/subtractions. The time domain truncation operation applied in (12) requires 4LK on multiplications and 5K on L − 2K on summations. After time domain truncation, the ADD-TT interpolation applies frequency and time domain averaging.…”

Joint TRFI and Deep Learning for Vehicular Channel Estimation

“…In our recent research [9], DNN is used as a post nonlinear processing unit after the conventional STA scheme. Conventional STA scheme updates the final estimate according to a linear combination between the previously estimated channel (6) and the frequency averaged channel estimates (5).…”

“…In our previous research work [9], we have proposed a novel optimized STA-DNN channel estimation scheme, where DNN is used as a post non-linear processing module to improve the conventional STA scheme performance by capturing more features of the time-frequency correlations of the channel samples and therefore, overcoming the problem of fixing the time and frequency averaging coefficients as performed in conventional STA scheme. STA-DNN schemes outperform conventional estimators and the AE-DNN especially in low SNR regions.…”

Channel Estimation with an Interpolation Trained Deep Neural Network

“…The DNN can directly learn the frequency domain information of the CSI. So it can perform noise reduction processing on the CSI estimated by the LS estimation and can be used for subsequent signal detection to avoid wasting resources by repeatedly sending pilots [10] [11] [12]. In the meantime, compressed sensing and deep learning can be combined to improve estimation accuracy [13] [14].…”