# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation
### Scenario Setup  
The evaluation scenario comprises one user connected to a mmWave base station, as depicted in Figure 1. Although having a greater number of users in the scenario may eventually impact the end-to-end performance, our interest lies on assessing whether it is feasible to learn the perceived congestion due to mmWave channel variations. In this sense, having a single user allows us to isolate the effects that the wireless channel variability would have over user traffic, especially if there were more active communications. More complex scenarios will be considered in the future, once the impact of the channel has been understood and characterized. Although there exist different channel models [39; 40], in this work we focus on the ones defined by the 3 GPP [41]. In particular, we apply the urban macro channel model, where both line-of-sight and non-line-of-sight situations may happen, according to the distance between user and base station [41]. In our scenario the user remains static, at a given distance from the base station, while the corresponding channel matrix is updated every millisecond. For the sake of simplicity, we assume that the antenna beam in the transmitter is perfectly aligned towards the receiver. As can be observed in Figure 1, we do not consider any limitation in the network from the base station to the application server, which is implemented using a 100 Gbps point-to-point link, but only in the cellular access network. To model the congestion at the wireless segment, caused by mmWave channel variations, we monitor the evolution of the buffers in the protocol stack. In particular, we focus on the Packet Data Convergence Protocol(RLC) transmission buffer, which increases when the number of transmission opportunities to the physical layer decreases, or when the Modulation and Coding Scheme (MCS) needs to be adapted due to poor channel conditions. Due to the relevance and potential impact of the RLC protocol in our study, it is worth discussing its purpose and possible configurations. Among other tasks, it is responsible for segmentation and reassembly of RLC Service Data Units (SDUs) coming from the Packet Data Convergence Protocol (PDCP) layer, see Figure 1. In addition, it ensures duplication detection and deletion, as well as in-order frame delivery to the upper layer. When configured in the Acknowledgment Mode (AM), RLC implements Automatic Repeat Request (ARQ), to provide reliable link-level communications, so that the connection finishes if the maximum number of retransmissions is surpassed. As for the MAC and link layers, we enable Hybrid Automatic Repeat Request (HARQ) in our setup, and RLC protocol is configured in the AM mode, since it is the most common choice. It is worth noting that with this configuration, losses over the wireless access segment are very unlikely. Hence, congestion-control mechanisms would be mainly triggered due to delay (i.e., Retransmission Timeout-RTO timer expiration in TCP), provided sufficiently long buffers exist in the rest of the network, thus ensuring there are no losses. At the application layer (user traffic) we consider a constant bit rate pattern, using User Datagram Protocol (UDP) as transport protocol, to avoid having congestion-control actions from happening, since they would impact the trace evolution, as was mentioned in Section 2. In this sense, the reason to use UDP is that our interest is the analysis of the relationship between congestion on the wireless access segment with the metrics that may be observed at the transport layer. Indeed, the use of UDP allows us to obtain raw information to study the learning possibilities of the different machine learning algorithms. If, on the other hand, we had used TCP, the particular congestion-control algorithm applied would have affected the analyzed parameters and the buffer status. For instance, slow-start phases due to bad channel conditions would lead to remarkable decrease of the traffic volume at the wireless access network.