# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 7 Conclusions  
The performance of transport protocols over wireless channels might be severely hindered by punctual congestion situations. These will become more relevant with the consolidation of 5G networks  
Figure 11: Comparison of the different algorithm for multi-level congestion. and, in particular, mmWave links. The scientific community is thus seeking new solutions that can appropriately deal with these situations. In this work, we have studied the potential benefits that Machine Learning techniques could bring. Using realistic channel traces, we have first identified metrics that could be used to predict the congestion level, while being observable from the transport layer. We have studied the correlation of both the delay and the inter-arrival time with the congestion level, which is established by looking at the RLC buffer occupancy. Afterwards, we assessed whether unsupervised and supervised ML techniques can appropriately predict congestion status. The latter are used as a benchmark, since the objective would be using unsupervised solutions, which impose less requirements and challenges towards an eventual implementation. The results show that the techniques that we analyzed yield reasonable precision, and they could thus be exploited in the design and implementation of congestion-control techniques suited for 5G networks. We also saw that the use of multi-level schemes does not bring a clear benefit, so enforcing a high granularity when identifying channel congestion levels might not be sensible. We saw that the performance of _k-means_ to perform binary classification is rather good, comparable to the supervised approach (SVC), but for high data rates. Furthermore, in multi-level congestion prediction (we established 4 different levels), the behavior of the hierarchical clustering based on _k-means_ is again comparable by that exhibited by supervised approaches (SVC and HMM), while the results of the RNN was relatively worse, and it had some convergence issues. There are three main lines of work that we will foster in our future research. We will first aim at improving the performance of the ML techniques, by considering different metrics and their combinations. In this regard, we will study non-linear relationship between transport layer metrics and congestion (such as mutual information) to elaborate more suitable metrics. This would be especially relevant for high data rates, where the obtained results exhibit poor linear relationship. In addition, we will explore other ML algorithms, keeping in mind the need to keep the complexity low. In particular, we will analyze how to enhance the convergence of the RNN algorithm, for instance using Gated Recurrent Units (GRU) mechanism, instead of LSTM. Furthermore, we will also broaden the scenario, by including heterogeneous deployments (LTE and 5G) leveraging MPTCP, more users (to better assess the influence of the scheduler, which would need to allocate resources), and we will include congestion situations on the transport part of the network, to challenge the learning capacity of the ML solutions, which would need to identify the congestion cause (access or transport segment). Conceptualization, L.D., M.K., Y.Z. and R.A.; methodology, L.D., M.K., Y.Z. and R.A.; software, A.F. ; validation, L.D., and A.F. ; formal analysis, A.F. ; writing--original draft preparation, A.F. and L.D. ; writing--review and editing, L.D., M.K. and Y.Z. ; funding acquisition. All authors have read and agreed to the published version of the manuscript. This research was funded by Ministerio de Economia, Industria y Competitividad (MINECO), Gobierno de Espana, grant number RTI2018-093475-A-100. This work was supported by the Spanish Government (MINECO) by means of the project FIBCE \"Future Internet Enabled Resilient smart CitiEs\" under Grant Agreement No. RTI2018-093475-A-I00. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: