# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 2 IEEE 802.11p Physical Layer Overview and System Model
### System Model  
This part presents the system model of the IEEE 802.11p V2V transmitter and receiver. Specifically, Figure 2a shows the block diagram of the IEEE 802.11p transmitter. The transmitter first performs error control coding over the information bits using convolutional encoder with code rate, \(c\). After that, the convolutionally encoded bits are modulated to the QAM symbols of order \(M\). We use \(x^{k}[n]\in\mathbb{C}\) to denote the QAM modulated symbol transmitted on the subcarrier \(n\) of the OFDM symbol \(k\in\{1,2,...,K\}\). Note that, the first five OFDM symbols (the preamble symbols and the signal field symbol), i.e., for \(k\in\{1,2,3,4,5\}\), are overhead and do not carry any payload data. Then, the IFFT is performed on the QAM modulated symbols and the cyclic prefixes (CP) are added to generate the OFDM symbols. It should be noted that, at the transmitter side, pilots are inserted within every OFDM symbol in order to facilitate both carrier frequency recovery and channel tracking at the receiver. The OFDM symbols are transmitted over the doubly selective V2V channel. Here, we assume a frequency selective and fast fading channel, where the channel fading effect on the subcarrier \(n\) of the OFDM symbol \(k\) is described by the coefficient \(H^{k}[n]\in\mathbb{C}\). The block diagram of the IEEE 802.11p receiver is shown in Figure 2b. The main operations implemented at the receiver are timing synchronization, frequency offset estimation, FFT, channel estimation, equalization, QAM demodulation and error control decoding. Particularly, the STSs are extracted from the frame preamble to perform frame timing synchronization and coarse frequency offset estimation and correction. Then, the CP of each OFDM symbol is removed and the FFT is preformed. After that, the frequency domain received symbol on the subcarrier \(n\) of the OFDM symbol \(k\) is denoted by \(y^{k}[n]\in\mathbb{C}\) that is given by  
\[y^{k}[n]=H^{k}[n]x^{k}[n]+v^{k}[n], \tag{3}\]  
where \(v^{k}[n]\in\mathbb{C}\) is the complex additive white Gaussian noise (AWGN) with zero mean and \(\sigma^{2}\) variance, i.e., \(v^{k}[n]\sim\mathcal{CN}(0,\sigma^{2})\). Figure 2: The block diagram of the IEEE 802.11p OFDM transmitter and receiver. (**a**) The IEEE 802.11p OFDM transmitter. (**b**) The proposed IEEE 802.11p OFDM receiver. Then, the receiver performs the fine frequency offset estimation and correction using the LTSs. After that, utilizing the LTSs part of the frame preamble, the receiver obtains the estimated noise variance, \(\hat{\sigma}^{2}\), and the estimated channel effect, \(\hat{H}_{p}[n]\). The channel estimates \(\hat{H}_{p}[n]\) are used for performing channel equalization on the received OFDM symbols \(y^{k}[n]\). At the same time, the channel estimates \(\hat{H}_{p}[n]\) for all subcarriers and the estimated noise standard deviation are used as the input features to a trained DCNN for predicting the suitable transmission mode class. Here, a transmission mode class refers to a combination of QAM modulation order, channel coding rate and data payload length. The predicted transmission mode class is sent to the transmitter to be considered for the next frame. After channel equalization, the QAM demodulation and error control decoding are executed to obtain the decoded data bits. In this paper, we consider two distinct channel equalization approaches. The first one is the traditional preamble-based channel equalization, where the channel estimates from the LTSs, \(\hat{H}_{p}[n]\), are used for equalizing all OFDM symbols in the frame. The second approach is the STA channel tracking [13] where the channel estimates are continuously updated for every OFDM symbol in the frame so as to alleviate the effect of outdated channel estimates used for equalizing the subsequent OFDM data symbols. The features of the current frame are extracted from the channel estimates \(\hat{H}_{p}[n]\) and noise standard deviation \(\hat{\sigma}\).