# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation
### Correlation Analysis  
Figures 4 and 5 illustrate the linear correlation of the RLC buffer occupancy with both the delay and the IaT for the greedy and max-weight schedulers, respectively. These results have been obtained over scenarios with different traffic rates, and distances between user and base station (as shown in the x-axis), for connections lasting 30 s. Traffic rates are chosen in the range \([10,1000]\) Mbps, and the distances are set to ensure that communication was not dropped due to timer expiration.  
Figure 4 shows the results that were obtained with the greedy scheduler. First, we can observe in Figure 3(a) that for data rates below 50 Mbps, the correlation between delay and congestion level is rather high for distances above 20 m. In addition, we can also observe that the correlation drops as we increase the application data rate. The results show that for data rates above 100 Mbps, the correlation stays below 0.5.  
Worthy of explanation is the behavior yielded by scenarios with short distances (10 and 20 m). In these cases, the probability of having line-of-sight conditions is very high, and thus the channel presents a large and stable communication capacity. Under these circumstances the buffer variation (albeit rather slight) is not related to capacity fluctuations, and the correlation values do not provide meaningful information. As can be observed in Figure 4, in general the correlation for short distances is very low, but for scenarios with 30 Mbps we can observe larger values. Similar behavior can be observed when using the max-weight scheduler, in Figure 5, for scenarios with 40 Mbps. All in all, correlation values for such short distances are not useful to predict the congestion state.  
Then, Figure 3(b) depicts the correlation values observed for the IaT. In this case, the results evince that the raw value of the IaT does not actually follow the buffer growing trend, as was already anticipated by the results discussed in Section 3. Regardless the distance or traffic rate, the values obtained are below 0.1, but for just one configuration.  
Then, Figure 5 shows the results that were obtained when we used the max-weight scheduler. As can be observed in Figure 4(a) the delay correlation exhibits a different trend, when compared with the one seen for the greedy scheduler. Although for low and medium data rates the correlation  
Figure 4: Linear correlation between the RLC buffer and transport level metrics using the greedy scheduler.  
is significantly lower, as we increase the traffic rate, it grows remarkably, even for small distances between user and base station. On the other hand, if we analyze the correlation corresponding to the IaT, Figure 5b, we can see a trend rather similar to that observed with the previous scheduler.  
These results indicate that as could have been expected, there exists a clear relationship between the congestion at the access segment and the delay experienced at the transport layer. Nevertheless, such relationship strongly depends on both the distance and on the application data rate. Furthermore, we have also observed that the usage of the physical resources established by the scheduler has a remarkable impact on such correlation. On the other hand, the raw IaT value does not yield a clear correlation with the congestion. In the following we will exploit the raw values of both IaT and delay to build derived metrics. Although the particular scheduler has some impact, we will restrict the results for the greedy scheduler for simplicity.