# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation
### Metrics Definition and Analysis  
Besides the raw parameters obtained from the scenario traces (delay and IaT) we have also identified a set of simple metrics, derived from such parameters, which may be used to improve the behavior of congestion learning. Apart from their accuracy, it becomes important to keep these metrics simple, so they could be computed by less capable devices. For that reason, we have focused on moving averages and standard deviations. It is worth noting that similar metrics have been already used to implement congestion-control solutions. For instance the soft-RTT calculation in [43] is estimated as a cumulative moving average, while the average standard deviation is used in the well-known Jacobson algorithm [44], as an approximation for the actual standard deviation in the RTT estimation. In particular, the following metrics have been studied:  
* \(\delta\): raw value of the delay. * \(\iota\): raw value of the IaT. * \(\overline{\delta}\): Moving average of the delay. Figure 5: Linear correlation between the RLC buffer and transport level metrics using the max-weight scheduler. * \(\bar{t}\): Moving average of the IaT. * \(\sigma_{\delta}\): Moving standard deviation of the delay. * \(\sigma_{t}\): Moving standard deviation of the IaT. For the moving average metrics, we exploit the Simple Moving Average (SMA) method. It is defined as the unweighted mean of \(\omega\) samples, where \(\omega\) is the sample window. For a sequence of samples of a variable \(X\), we define the moving average obtained at the \(n^{th}\) sample as \(\overline{X}_{n}=\frac{1}{\omega}\sum_{i=0}^{\omega-1}X_{n-i}\). Similarly, the moving standard deviation obtained for the \(n^{th}\) sample of a variable \(X\) is defined as \(\sigma_{X_{n}}=\sqrt{\frac{\sum_{i=n-\omega+1}^{n}\left(X_{n}-\overline{X} \right)^{2}}{\omega-1}}\), where \(\omega\) is again the corresponding window length. In Figure 6 we illustrate the correlation values obtained for the different metrics when we fix either the distance or the data rate. In particular, Figure 5(a) shows the results observed for a distance of 40 m and different data rates, and Figure 5(b) depicts the performance obtained with a data rate of 50 Mbps and for various distances. The former results show that the delay correlation is much higher than that yielded by the IaT. However, the average value of the IaT (\(\bar{t}\)) seems to be a better metric than the one corresponding to the delay (\(\bar{\delta}\)). Then, if we look at the moving standard deviation, we can see that for both delay and IaT there is a clear correlation with the congestion, especially for low data rates. In the case of the delay, this behavior was expected, as the delay metric is strongly related to the congestion; however, that trend was not observed for the IaT. On the other hand, we saw in Section 3 that the IaT exhibited some peaks, in many cases aligned with times when the buffer started to grow. This behavior is the reason of the high correlation values between the moving standard deviation of the IaT and the congestion experienced in the buffer, which at the transport layer is perceived as _jitter_. The scenarios with fixed data rate, shown in Figure 5(b), yield a similar trend. The moving average metric improves the correlation of the IaT, while it is not a good option for the delay. In addition, the moving standard deviation of the IaT shows rather good results, as compared with its raw value. It is worth noting that as we observed in the previous section, the correlation values for short distances  
Figure 6: Correlation of all metrics with congestion. (10 and 20 m) are very low, since the user experiences very good channel conditions, so small buffer variations do not impact transport layer performance. Finally, we have also analyzed the cross-correlation between the different metrics, to know whether their combined use would yield additional benefits to the learning process.