# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
Furthermore, they reported that their spectral temporal averaging (STA) technique gives the best performance compared to other equalization techniques presented in their paper. However, the work in [12] considered only the MCS of the lowest data rate, namely \(MCS_{0}\), where the binary phase shift keying (BPSK) modulation and \(\frac{1}{2}\) channel coding rate were assumed. Furthermore, they did not consider the effect of the frame length. In [13], the authors extended the scheme of [12] to different MCSs of higher data rates and showed that the STA channel tracking algorithm outperforms the traditional preamble-based LS channel estimation. Moreover, they have studied the effect of different fixed payload lengths with different fixed MCSs on the V2V links. However, they did not study the effect of link adaptation schemes in either the MCS or the payload length on the performance of V2V communications. Recently, the application of machine learning to vehicular networks has attracted the attention of the research community [15; 16]. In this paper, we present a joint adaptive MCS and payload length selection (AMCPLS) scheme. The proposed AMCPLS attempts to select the payload length, the modulation order, and the channel coding rate based on the V2V channel condition so as to achieve both reliability and high effective throughput. While the concept of the AMCPLS can be applied to both the 5G-V2V and the DSRC IEEE 802.11p V2V, in this paper, we will concentrate on the DSRC IEEE 802.11p V2V since its standard is more well-established and its specifications are more well-defined [17; 18]. Furthermore, we are concentrating on safety-related applications and it has been reported in the literature that the DSRC IEEE 802.11p V2V outperforms the 4G-LTE V2V for the safety-related applications [19; 20]. The contribution of this paper can be summarized as follows. * First, we develop a novel link adaptation scheme for V2V communications, namely the AMCPLS scheme, that enables transmitters to adaptively select not only the MCS but also the payload length of the transmission frame. We consider practical receivers that involve realistic timing synchronization, carrier frequency offset correction and channel estimation. The proposed AMCPLS scheme is evaluated under traditional preamble-based LS channel estimation [14] and STA channel tracking [13]. In both cases, our adaptive AMCPLS scheme outperforms the non-adaptive schemes in terms of FER and effective throughput. * Moreover, we propose a machine learning framework for the proposed AMCPLS scheme, where past observations of channel state information (CSI) along with achieved FER and effective throughput are used as training data. This training data can be utilized to predict the best combination of the payload length, the modulation order, and the channel coding rate for future V2V transmissions by leveraging machine learning tools. Specifically, a deep convolutional neural network (DCNN) has been designed for implementing the proposed AMCPLS scheme. Our DCNN-based AMCPLS scheme can achieve better performance compared to other machine learning based schemes, (such as k-NN and SVM). We performed extensive numerical evaluations to verify our DCNN-based AMCPLS scheme. The remainder of this paper is organized as follows. Section 2 presents an overview of the IEEE 802.11p standard and the considered system model. In Section 3, we discuss the joint adaptive MCS and payload length scheme for V2V communications. Then, Section 4 presents our deep learning approach for AMCPLS. After that, simulation results are shown in Section 5. Finally, Section 6 concludes the paper.