# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 2 Related Work
The authors point out the importance of efficiently managing mobility to improve the TCP performance in terms of both throughput and delay. In addition, they evince that even with a very efficient mobility management scheme, TCP is very sensitive to end-to-end delay, which strongly impacts both users and network. Although 5G mmWave would certainly boost the capacity of forthcoming cellular network deployments, it will coexist with other technologies, such as LTE. In these heterogeneous scenarios traffic could be offloaded to LTE networks, if mmWave coverage degrades. This possibility has been studied in different works by exploiting multi-path TCP (MPTCP). For instance, [PERSON] et al. [22] propose an offloading technique (offloading by restriction - OBR) to optimize the throughput in 5G and LTE heterogeneous networks. Following a similar approach, the performance of Linux MPTCP over LTE and mmWave links is analyzed in [23]. In particular, the authors seek the optimal configuration of secondary paths and how to select the corresponding congestion-control algorithms. Furthermore, [PERSON] et al. analyze in [24] different coupling solutions for MPTCP over dual-band 60 GHz/5 GHz links, using off-the-shelf hardware. As can be seen, the goal of these works is different to ours, since we do not consider multi-path. On the other hand, none of them exploit machine learning techniques. We will consider the integration of multi-path within the proposed learning scheme in our future work. Although TCP, and its various modifications and extensions, stands out as the most widespread solution at the transport layer, Quick UDP Internet Connections (QUIC) has recently appeared as an appealing alternative, especially for web-based traffic. However, the negative impact of wireless channels over congestion-control algorithms persists. In this sense, [PERSON] et al. introduced in [25] a cross-layer QUIC enhancement to optimize handover decisions considering wireless channel variations in 5G mmWave scenarios. It is also worth noting that there exist a few works that have applied machine learning both to predict the congestion level and to develop novel congestion-control solutions. For instance, [PERSON] et al. analyzed in [PERSON] et al. (2019) the performance of different machine learning algorithms to build a loss classifier over random topologies. In this regard, their research shares some of our goals. However, it was not applied to mmWave networks, while we focus on delay, rather than on loss. In addition, the authors of [PERSON] et al. (2019) analyze congestion learnability in general, but they do not consider the particular features of mmWave channels. On the other hand, [PERSON] et al. (2019) addresses congestion learnability over mmWave networks, but the network emulation that was used does not consider the relationship between congestion and other protocol stack elements from the cellular access network. All in all, we can conclude that the use of machine learning techniques to learn congestion situations over mmWave channels has not been thoroughly addressed yet.