# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 5 Numerical Results
### Performance Evaluation of Machine Learning Based Amcpls  
In this part, we evaluate the proposed DCNN based AMCPLS approach. The k-NN based AMCPLS and the SVM based AMCPLS are also evaluated and their performance are used as benchmarks. We perform simulations for IEEE 802.11p V2V communication systems in the rural LOS environment. The training examples \(\{f^{m},a^{m}\}_{m=1}^{M}\) are generated as follows. We simulate the IEEE 802.11p V2V system at different SNR values that range from 15 dB to 40 dB with a 1 dB step. For every SNR value, transmission using all possible classes (indexed from 0 to 23) is performed for different 1000 channel realizations and the FER of each class is calculated. Then, the ideal class, that corresponds to the maximum throughput and at the same time satisfies the target FER, \(\epsilon=0.05\), is selected as the label for the channel realizations of the corresponding SNR. For every frame received correctly, the features \(f^{m}\) are computed and saved along with the corresponding label \(a^{m}\). The feature set extracted as in (9) is used to train the DCNN, k-NN, and SVM for AMCPLS.  
After we have finished the training, in order to evaluate the proposed DCNN AMCPLS approach, we generate new 1000 channel realizations for each SNR value that ranges from 15 dB to 40 dB with a 0.5 dB step. Transmissions are performed over all channel realizations. At the receiver, the features are computed from the received signal. Then, the features are used as the input to the trained DCNN, k-NN, and SVM for predicting the corresponding class, i.e., optimal combination of MCS and payload length that should be utilized in the next frame.  
Figure 10 depicts the FER and effective throughput performance of the proposed DCNN AMCPLS, k-NN AMCPLS, and SVM AMCPLS when both traditional LS channel estimation and STA channel tracking are used. Particularly, Figure 10a shows that k-NN AMCPLS and SVM AMCPLS failed to fulfill the target FER constraint for a considerable range of SNR values when both traditional LS channel estimation and STA channel tracking are considered. On the other hand, the proposed DCNN AMCPLS approach has better FER performance compared to both k-NN AMCPLS and SVM AMCPLS for both the traditional LS channel estimation and STA channel tracking. From Figure 10b, it is noted that the k-NN AMCPLS and SVM AMCPLS can provide higher throughput than the proposed DCNN  
Figure 9: Comparison among the proposed AMCPLS and the fixed 500 Bytes payload length at different MCSs when STA channel tracking is used in the rural LOS V2V environment.  
AMCPLS at some ranges of SNR values for both traditional LS channel estimation and STA channel tracking at the price of the increased FER. The increased FERs of both k-NN AMCPLS and SVM AMCPLS affect their reliability in V2V communications and requires multiple frame retransmissions that affects the system latency and energy efficiency.  
Figure 10: Comparison among the proposed DCNN AMCPLS, k-NN AMCPLS, and SVM AMCPLS for both traditional LS channel estimation and STA channel tracking in the rural LOS V2V environment.  
In order to assess the computational cost of the DCNN AMCPLS, the k-NN AMCPLS, and the SVM AMCPLS, we evaluated the average time required by each approach for predicting the transmission mode class of more than 49,000 channel realizations on an Intel core i3-6100 with speed 3.7 GHz processor. We found that the average prediction times of the DCNN AMCPLS, k-NN AMCPLS, and SVM AMCPLS are 64 us, 480 us, and 1194 us, respectively. It is clear that the proposed DCNN AMCPLS has reduced prediction time and reduced FER compared to both k-NN AMCPLS and SVM AMCPLS. This means the proposed DCNN AMCPLS is more reliable than both k-NN AMCPLS and SVM AMCPLS for safety related related applications, where the increased FER and the increased transmission mode class prediction time may lead to severe consequences such as cars collisions.