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.
Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.
Abstract-The services foreseen for 5G networks will demand a vast number of challenging requirements to be fulfilled by the physical layer. These services can be grouped into application scenarios, each one with a key requirement to be addressed by the 5G network. A flexible waveform associated with a appropriate data frame is an essential feature in order to guarantee the support of contrasting requirements from different application scenarios such as Enhanced Mobile Broadband, Internet of Things, Tactile Internet and Internet Access for Remote Areas. In this paper, we propose a flexible data frame based on Generalized Frequency Division Multiplexing (GFDM) that can be tailored to address the specific key requirements of the different 5G scenarios. The paper also presents the physical layer parametrization that can be used for each application.
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