# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 5 Numerical Results
### Performance of Ideal AMCPLS  
Practically, the ideal AMCPLS expressed in (8) can not be implemented as we do not know the actual channel condition and the FER. However, we can evaluate it using numerical enumerations, i.e., we try all possible combinations of the MCSs and payload lengths at every SNR value; then, we select the combination of the MCS and payload length that achieves the maximum effective throughput and satisfies the predefined FER. In this regard, we evaluate the performance of ideal AMCPLS, where we assume that both the actual channel and the FER can be obtained beforehand (the FER is obtained from simulations using the actual channel) and thus the ideal combination of the MCS and the payload length can be determined for the transmission of the current frame. In this part, we perform simulations to evaluate the ideal AMCPLS scheme considering both the traditional LS channel estimation [14] and the STA channel tracking [13]. We consider the V2V communication in the rural LOS environment at different signal to noise ratios (SNR) that range from 15 dB to 40 dB. Specifically, Figures 4-6 depict the FERs and effective throughputs of the proposed \begin{table}
\begin{tabular}{c c} \hline \hline Carrier frequency & 5.9 GHz \\ \hline Channel bandwidth & 10 MHz \\ \hline OFDM symbol duration & 8 \(\upmu\)s \\ \hline IFFT period & 6.4 \(\upmu\)s \\ \hline Gaurd interval & 1.6 \(\upmu\)s \\ \hline The total number of subcarriers & 64 \\ \hline The number of data subcarriers & 48 \\ \hline The number of pilot subcarriers & 4 \\ \hline The number of null carriers & 12 \\ \hline The number of short training symbols & 10 \\ \hline The duration of a short training symbol & 1.6 \(\upmu\)s \\ \hline The number of long training symbols & 2 \\ \hline The duration of a long training symbol & 6.4 \(\upmu\)s \\ \hline Target FER & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. AMCPLS scheme compared to fixed MCSs with fixed payload lengths of 100, 300, and 500 Bytes, respectively, when the traditional LS channel estimation is employed. It can be observed from the results that for most of the fixed schemes, the FER decreases and the throughput increases as the SNR increases. Furthermore, increasing the payload length when a specific MCS is utilized leads to an increase in the effective throughput due to the overhead reduction; at the same time, the FERs for most of the fixed MCSs increase as well, which impact the reliability and energy efficiency of the V2V communication link. On the other hand, the proposed AMCPLS scheme always satisfies the predefined target FER, \(\epsilon=0.05\) and provides satisfactory throughputs. Similarly, Figures 7-9 show the FERs and effective throughputs of the proposed AMCPLS scheme compared to fixed MCSs with fixed payload lengths of 100, 300, and 500 Bytes, respectively, when the STA channel tracking is utilized. It is noteworthy that, for fixed MCS with fixed payload length schemes, the improvements in the FERs and throughputs of the STA channel tracking compared to that of the traditional LS channel estimation appears significantly at high SNR values especially when a long payload length is used. For the AMCPLS, the FER constraint is always satisfied for both the STA channel tracking and the traditional LS channel estimation. The AMCPLS when combined with the STA channel tracking can provide significant improvement in the effective physical layer throughput when compared to traditional LS channel estimation, specifically at high SNR values. It is noteworthy that the effective throughput of the ideal AMCPLS scheme sometimes fluctuates even for an increase in the average SNR value when both LS channel estimation and STA channel tracking are considered as depicted in Figures 4b-9b. These fluctuations are for small SNR variations where the fading effect has the dominant impact on the channel condition.