# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks  
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getting a look up table that cover the whole range of link quality metrics will be prohibitively huge. Recently, learning-based approaches are employed to improve the effectiveness of wireless link adaptations. The authors in [6] proposed a supervised learning approach for adaptive modulation and coding (AMC) in the IEEE 802.11n wireless local area networks (WLANs). The target is to select the best modulation and coding scheme (MCS) in order to maximize the throughput under certain frame error rate (FER) constraint. They have utilized the k-nearest neighbors (k-NN) classification algorithm for AMC. In [7], the authors solved the same AMC problem using the algorithm of support vector machines (SVM). However, the works in [6; 7] both assumed nonrealistic assumptions such as fixed frame length, perfect synchronization, and perfect channel knowledge. The work in [8] presented a deep convolutional neural network (DCNN) approach for AMC in MIMO-OFDM systems. The authors considered practical timing synchronization, carrier frequency offset and channel estimation at receivers. However, the authors did not consider the effect of frame length. Furthermore, they evaluated their approach for indoor WLANs applications. It should be noted that the V2V communications are more challenging compared to the indoor WLANs. The V2V communication channel is doubly selective (i.e., in both of the frequency and the time domains). This means that the channel fluctuates considerably within the transmission bandwidth (frequency-selective fading) and within the frame duration (fast fading) [9]. This is due to the combined effects of the multipath nature of V2V propagation channels, along with the high mobility of radio terminals in the vehicles and some scatterers around [10]. Thus, advanced link adaptation schemes are crucial for V2V communications in order to deal with the varying V2V links under the stringent V2V communication requirements, such as high effective throughput and reliability. For instance, an adaptive frame length scheme for vehicular networks has been presented in [11]. The authors in [11] show that the adaptive frame length scheme outperforms non-adaptive frame length schemes for vehicular networks in terms of throughput, retransmissions, and overheads. However, the authors in [11] did not consider the effect of the MCS. Another challenging task for V2V communications is the channel estimation. In traditional channel estimation techniques, transmitters add training sequences, i.e., preamble, at the beginning of each frame; then, receivers can utilize this preamble to estimate the channel and use the channel estimates to decode the following data symbols in the same frame. However, this is not optimal for a quickly varying V2V channel, where the channel estimates from the preamble at the beginning of the frame may be significantly different from the actual channels encountered by the subsequent symbols within the same frame. As a consequence, utilizing the initial channel estimates from the preamble to decode the subsequent symbols in the frame may lead to decoding errors. This leads to a significant degradation in the frame error rate performance of V2V links. Therefore, channel tracking techniques have been presented such as [12; 13], where the initial channel estimates from the training sequences of the preamble are updated within the duration of the frame. In particular, the work in [12] presents a number of dynamic data-aided equalization techniques. The authors in [12] evaluated these techniques by empirical experiments in practical V2V environments and concluded that their equalization techniques outperform traditional preamble-based least squares (LS) [14] and pilot aided equalizers.