Abstract
Recently, vehicular networks have emerged to facilitate intelligent transportation systems (ITS). They enable vehicles to communicate with each other in order to provide various services such as traffic safety, autonomous driving, and entertainments. The vehicle-to-vehicle (V2V) communication channel is doubly selective, where the channel changes within the transmission bandwidth and the frame duration. This necessitates robust algorithms to provide reliable V2V communications. In this paper, we propose a scheme that provides joint adaptive modulation, coding and payload length selection (AMCPLS) for V2V communications. Our AMCPLS scheme selects both the modulation and coding scheme (MCS) and the payload length of transmission frames for V2V communication links, according to the V2V channel condition. Our aim is to achieve both reliability and spectrum efficiency. Our proposed AMCPLS scheme improves the V2V effective throughput performance while satisfying a predefined frame error rate (FER). Furthermore, we present a deep learning approach that exploits deep convolutional neural networks (DCNN) for implementing the proposed AMCPLS. Simulation results reveal that the proposed DCNN-based AMCPLS approach outperforms other competing machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM) in terms of FER, effective throughput, and prediction time.
Highlights
Vehicular networks enable various applications such as road safety, traffic jam reporting, public aid units guiding, entertainments, and autonomous driving, etc. [1,2]
We propose a machine learning framework for the proposed AMCPLS scheme, where past observations of channel state information (CSI) along with achieved frame error rate (FER) and effective throughput are used as training data
We compare the performance of our proposed deep convolutional neural network (DCNN) based AMCPLS with the performance of other rival machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM)
Summary
Vehicular networks enable various applications such as road safety, traffic jam reporting, public aid units guiding, entertainments, and autonomous driving, etc. [1,2]. Vehicular networks enable various applications such as road safety, traffic jam reporting, public aid units guiding, entertainments, and autonomous driving, etc. Vehicular networks aim to provide sustainable and reliable communications for smart vehicles. They are basic building blocks in the cooperative intelligent transportation systems (ITS) of future smart cities. The vehicle-to-vehicle (V2V) communication in vehicular networks was standardized in the form of an amendment to the IEEE 802.11 standard. The IEEE 802.11p defines V2V augmentations to the IEEE 802.11 standard in order to enable dedicated short-range communication (DSRC) for vehicular networks [3]. The varying nature of wireless channels calls for link adaptations in order to improve both the spectrum efficiency and the communication reliability. There are many link adaptation schemes developed using traditional model-based approaches (e.g., [4,5])
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